data research project ideas

12 Data Science Projects for Beginners and Experts

Data science is a booming industry. Try your hand at these projects to develop your skills and keep up with the latest trends.

Data science is a profession that requires a variety of scientific tools, processes, algorithms and knowledge extraction systems that are used to identify meaningful patterns in structured and unstructured data alike.

If you fancy data science and are eager to get a solid grip on the technology, now is as good a time as ever to hone your skills to comprehend and manage the upcoming challenges facing the profession. The purpose behind this article is to share some practicable ideas for your next project, which will not only boost your confidence in data science but also play a critical part in enhancing your skills .

12 Data Science Projects to Experiment With

• Building chatbots.
• Credit card fraud detection.
• Fake news detection.
• Forest fire prediction.
• Classifying breast cancer.
• Driver drowsiness detection.
• Recommender systems.
• Sentiment analysis.
• Exploratory data analysis.
• Gender detection and age detection.
• Recognizing speech emotion.
• Customer segmentation.

Top Data Science Projects

Understanding data science can be quite confusing at first, but with consistent practice, you’ll start to grasp the various notions and terminologies in the subject. The best way to gain more exposure to data science apart from going through the literature is to take on some helpful projects that will upskill you and make your resume more impressive.

In this section, we’ll share a handful of fun and interesting project ideas with you spread across all skill levels ranging from beginners to intermediate to veterans.

More on Data Science: How to Build Optical Character Recognition (OCR) in Python

1. Building Chatbots

• Language: Python
• Data set: Intents JSON file
• Source code: Build Your First Python Chatbot Project

Chatbots play a pivotal role for businesses as they can effortlessly   without any slowdown. They automate a majority of the customer service process,  single-handedly reducing the customer service workload. The chatbots utilize a variety of techniques backed with artificial intelligence, machine learning and data science.

Chatbots analyze the input from the customer and reply with an appropriate mapped response. To train the chatbot, you can use recurrent neural networks with the intents JSON dataset , while the implementation can be handled using Python . Whether you want your chatbot to be domain-specific or open-domain depends on its purpose. As these chatbots process more interactions, their intelligence and accuracy also increase.

2. Credit Card Fraud Detection

• Language: R or Python
• Data set: Data on the transaction of credit cards is used here as a data set.
• Source code: Credit Card Fraud Detection Using Python

Credit card fraud is more common than you think, and lately, they’ve been on the rise. We’re on the path to cross a billion credit card users by the end of 2022. But thanks to the innovations in technologies like artificial intelligence, machine learning and data science, credit card companies have been able to successfully identify and intercept these frauds with sufficient accuracy.

Simply put, the idea behind this is to analyze the customer’s usual spending behavior, including mapping the location of those spendings to identify the fraudulent transactions from the non-fraudulent ones. For this project, you can use either R or Python with the customer’s transaction history as the data set and ingest it into decision trees , artificial neural networks , and logistic regression . As you feed more data to your system, you should be able to increase its overall accuracy.

3. Fake News Detection

• Data set/Packages: news.csv
• Source code: Detecting Fake News

Fake news needs no introduction. In today’s connected world, it’s become ridiculously easy to share fake news over the internet. Every once in a while, you’ll see false information being spread online from unauthorized sources that not only cause problems to the people targeted but also has the potential to cause widespread panic and even violence.

To curb the spread of fake news, it’s crucial to identify the authenticity of information, which can be done using this data science project. You can use Python and build a model with TfidfVectorizer and PassiveAggressiveClassifier to separate the real news from the fake one. Some Python libraries best suited for this project are pandas, NumPy and scikit-learn . For the data set, you can use News.csv.

4. Forest Fire Prediction

Building a forest fire and wildfire prediction system is another good use of data science’s capabilities. A wildfire or forest fire is an uncontrolled fire in a forest. Every forest wildfire has caused an immense amount of damage to  nature, animal habitats and human property.

To control and even predict the chaotic nature of wildfires, you can use k-means clustering to identify major fire hotspots and their severity. This could be useful in properly allocating resources. You can also make use of meteorological data to find common periods and seasons for wildfires to increase your model’s accuracy.

More on Data Science: K-Nearest Neighbor Algorithm: An Introduction

5. Classifying Breast Cancer

• Data set: IDC (Invasive Ductal Carcinoma)
• Source code: Breast Cancer Classification with Deep Learning

If you’re looking for a healthcare project to add to your portfolio, you can try building a breast cancer detection system using Python. Breast cancer cases have been on the rise, and the best possible way to fight breast cancer is to identify it at an early stage and take appropriate preventive measures.

To build a system with Python, you can use the invasive ductal carcinoma (IDC) data set, which contains histology images for cancer-inducing malignant cells. You can train your model with it, too. For this project, you’ll find convolutional neural networks are better suited for the task, and as for Python libraries, you can use NumPy , OpenCV , TensorFlow , Keras, scikit-learn and Matplotlib .

6. Driver Drowsiness Detection

• Source code: Driver Drowsiness Detection System with OpenCV & Keras

Road accidents take many lives every year, and one of the root causes of road accidents is sleepy drivers. One of the best ways to prevent this is to implement a drowsiness detection system.

A driver drowsiness detection system that constantly assesses the driver’s eyes and alerts them with alarms if the system detects frequently closing eyes is yet another project that has the potential to save many lives .

A webcam is a must for this project in order for  the system to periodically monitor the driver’s eyes. This Python project will require a deep learning model and libraries such as OpenCV , TensorFlow , Pygame , and Keras .

More on Data Science: 8 Data Visualization Tools That Every Data Scientist Should Know

7. Recommender Systems (Movie/Web Show Recommendation)

• Language: R
• Data set: MovieLens
• Packages: Recommenderlab, ggplot2, data.table, reshape2
• Source code: Movie Recommendation System Project in R

Have you ever wondered how media platforms like YouTube, Netflix and others recommend what to watch next? They use a tool called the recommender/recommendation system . It takes several metrics into consideration, such as age, previously watched shows, most-watched genre and watch frequency, and it feeds them into a machine learning model that then generates what the user might like to watch next.

Based on your preferences and input data, you can try to build either a content-based recommendation system or a collaborative filtering recommendation system. For this project, you can use R with the MovieLens data set, which covers ratings for over 58,000 movies. As for the packages, you can use recommenderlab , ggplot2 , reshap2 and data.table.

8. Sentiment Analysis

• Data set: janeaustenR
• Source code: Sentiment Analysis Project in R

Also known as opinion mining, sentiment analysis is a tool backed by artificial intelligence, which essentially allows you to identify, gather and analyze people’s opinions about a subject or a product. These opinions could be from a variety of sources, including online reviews or survey responses, and could span a range of emotions such as happy, angry, positive, love, negative, excitement and more.

Modern data-driven companies benefit the most from a sentiment analysis tool as it gives them the critical insight into the people’s reactions to the dry run of a new product launch or a change in business strategy. To build a system like this, you could use R with janeaustenR’s data set along with the tidytext package .

9. Exploratory Data Analysis

• Packages: pandas, NumPy, seaborn, and matplotlib
• Source code: Exploratory data analysis in Python

Data analysis starts with exploratory data analysis (EDA). It plays a key role in the data analysis process as it helps you make sense of your data and often involves visualizing them for better exploration. For visualization , you can pick from a range of options, including histograms, scatterplots or heat maps. EDA can also expose unexpected results and outliers in your data. Once you have identified the patterns and derived the necessary insights from your data, you are good to go.

A project of this scale can easily be done with Python, and for the packages, you can use pandas, NumPy, seaborn and matplotlib.

A great source for EDA data sets is the IBM Analytics Community .

10. Gender Detection and Age Prediction

• Data set: Adience
• Packages: OpenCV
• Source code: OpenCV Age Detection with Deep Learning

Identified as a classification problem, this gender detection and age prediction project will put both your machine learning and computer vision skills to the test. The goal is to build a system that takes a person’s image and tries to identify their age and gender.

For this project, you can implement convolutional neural networks and use Python with the OpenCV package . You can grab the Adience dataset for this project. Factors such as makeup, lighting and facial expressions will make this challenging and try to throw your model off, so keep that in mind.

11. Recognizing Speech Emotions

• Data set: RAVDESS
• Packages: Librosa, Soundfile, NumPy, Sklearn, Pyaudio
• Source code: Speech Emotion Recognition with librosa

Speech is one of the most fundamental ways of expressing ourselves, and it contains a variety of emotions, such as calmness, anger, joy and excitement, to name a few. By analyzing the emotions behind speech, it’s possible to use this information to restructure our actions,  services and even products, to offer a more personalized service to specific individuals.

This project involves identifying and extracting emotions from multiple sound files containing human speech. To make something like this in Python, you can use the Librosa , SoundFile , NumPy, Scikit-learn, and PyAaudio packages. For the data set, you can use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) , which contains over 7300 files.

12. Customer Segmentation

• Source code: Customer Segmentation using Machine Learning

Modern businesses strive by delivering highly personalized services to their customers, which would not be possible without some form of customer categorization or segmentation. In doing so, organizations can easily structure their services and products around their customers while targeting them to drive more revenue.

For this project, you will use unsupervised learning to group your customers into clusters based on individual aspects such as age, gender, region, interests, and so on. K-means clustering or hierarchical clustering are suitable here, but you can also experiment with fuzzy clustering or density-based clustering methods. You can use the Mall_Customers data set as sample data.

More Data Science Project Ideas to Build

• Visualizing climate change.
• Uber’s pickup analysis.
• Web traffic forecasting using time series.
• Impact of Climate Change On Global Food Supply.
• Detecting Parkinson’s disease.
• Pokemon data exploration.
• Earth surface temperature visualization.
• Brain tumor detection with data science.
• Predictive policing.

Throughout this article, we’ve covered 12 fun and handy data science project ideas for you to try out. Each will help you understand the basics of data science technology. As one of the hottest, in-demand professions in the industry, the future of data science holds many promises. But to make the most out of the upcoming opportunities, you need to be prepared to take on the challenges it brings.

Frequently Asked Questions

What projects can be done in data science.

• Build a chatbot using Python.
• Create a movie recommendation system using R.
• Detect credit card fraud using R or Python.

How do I start a data science project?

To start a data science project, first decide what sort of data science project you want to undertake, such as data cleaning, data analysis or data visualization. Then, find a good dataset on a website like data.world or data.gov. From there, you can analyze the data and communicate your results.

How long does a data science project take to complete?

Data science projects vary in length and depend on several variables like the data source, the complexity of the problem you’re trying to solve and your skill level. It could take a few hours or several months.

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21 Data Science Projects for Beginners (with Source Code)

Looking to start a career in data science but lack experience? This is a common challenge. Many aspiring data scientists find themselves in a tricky situation: employers want experienced candidates, but how do you gain experience without a job? The answer lies in building a strong portfolio of data science projects .

A well-crafted portfolio of data science projects is more than just a collection of your work. It's a powerful tool that:

• Shows your ability to solve real-world problems
• Highlights your technical skills
• Proves you're ready for professional challenges
• Makes up for a lack of formal work experience

By creating various data science projects for your portfolio, you can effectively demonstrate your capabilities to potential employers, even if you don't have any experience . This approach helps bridge the gap between your theoretical knowledge and practical skills.

Why start a data science project?

Simply put, starting a data science project will improve your data science skills and help you start building a solid portfolio of projects. Let's explore how to begin and what tools you'll need.

Steps to start a data science project

• Define your problem : Clearly state what you want to solve .
• Gather and clean your data : Prepare it for analysis.
• Explore your data : Look for patterns and relationships .

Hands-on experience is key to becoming a data scientist. Projects help you:

• Apply what you've learned
• Develop practical skills
• Show your abilities to potential employers

Common tools for building data science projects

To get started, you might want to install:

• Programming languages : Python or R
• Data analysis tools : Jupyter Notebook and SQL
• Version control : Git
• Machine learning and deep learning libraries : Scikit-learn and TensorFlow , respectively, for more advanced data science projects

These tools will help you manage data, analyze it, and keep track of your work.

Overcoming common challenges

New data scientists often struggle with complex datasets and unfamiliar tools. Here's how to address these issues:

• Start small : Begin with simple projects and gradually increase complexity.
• Use online resources : Dataquest offers free guided projects to help you learn.
• Join a community : Online forums and local meetups can provide support and feedback.

Setting up your data science project environment

To make your setup easier :

• Use Anaconda : It includes many necessary tools, like Jupyter Notebook.
• Implement version control: Use Git to track your progress .

Skills to focus on

According to KDnuggets , employers highly value proficiency in SQL, database management, and Python libraries like TensorFlow and Scikit-learn. Including projects that showcase these skills can significantly boost your appeal in the job market.

In this post, we'll explore 21 diverse data science project ideas. These projects are designed to help you build a compelling portfolio, whether you're just starting out or looking to enhance your existing skills. By working on these projects, you'll be better prepared for a successful career in data science.

Choosing the right data science projects for your portfolio

Building a strong data science portfolio is key to showcasing your skills to potential employers. But how do you choose the right projects? Let's break it down.

Balancing personal interests, skills, and market demands

When selecting projects, aim for a mix that :

• Aligns with your interests
• Matches your current skill level
• Highlights in-demand skills
• Projects you're passionate about keep you motivated.
• Those that challenge you help you grow.
• Focusing on sought-after skills makes your portfolio relevant to employers.

For example, if machine learning and data visualization are hot in the job market, including projects that showcase these skills can give you an edge.

A step-by-step approach to selecting data science projects

• Assess your skills : What are you good at? Where can you improve?
• Identify gaps : Look for in-demand skills that interest you but aren't yet in your portfolio.
• Plan your projects : Choose 3-5 substantial projects that cover different stages of the data science workflow. Include everything from data cleaning to applying machine learning models .
• Get feedback and iterate : Regularly ask for input on your projects and make improvements.

Common data science project pitfalls and how to avoid them

Many beginners underestimate the importance of early project stages like data cleaning and exploration. To overcome data science project challeges :

• Spend enough time on data preparation
• Focus on exploratory data analysis to uncover patterns before jumping into modeling

By following these strategies, you'll build a portfolio of data science projects that shows off your range of skills. Each one is an opportunity to sharpen your abilities and demonstrate your potential as a data scientist.

Real learner, real results

Take it from Aleksey Korshuk , who leveraged Dataquest's project-based curriculum to gain practical data science skills and build an impressive portfolio of projects:

The general knowledge that Dataquest provides is easily implemented into your projects and used in practice.

Through hands-on projects, Aleksey gained real-world experience solving complex problems and applying his knowledge effectively. He encourages other learners to stay persistent and make time for consistent learning:

I suggest that everyone set a goal, find friends in communities who share your interests, and work together on cool projects. Don't give up halfway!

Aleksey's journey showcases the power of a project-based approach for anyone looking to build their data skills. By building practical projects and collaborating with others, you can develop in-demand skills and accomplish your goals, just like Aleksey did with Dataquest.

21 Data Science Project Ideas

Excited to dive into a data science project? We've put together a collection of 21 varied projects that are perfect for beginners and apply to real-world scenarios. From analyzing app market data to exploring financial trends, these projects are organized by difficulty level, making it easy for you to choose a project that matches your current skill level while also offering more challenging options to tackle as you progress.

Beginner Data Science Projects

• Profitable App Profiles for the App Store and Google Play Markets
• Exploring Hacker News Posts
• Exploring eBay Car Sales Data
• Finding Heavy Traffic Indicators on I-94
• Storytelling Data Visualization on Exchange Rates
• Clean and Analyze Employee Exit Surveys
• Star Wars Survey

Intermediate Data Science Projects

• Exploring Financial Data using Nasdaq Data Link API
• Popular Data Science Questions
• Investigating Fandango Movie Ratings
• Finding the Best Markets to Advertise In
• Mobile App for Lottery Addiction
• Building a Spam Filter with Naive Bayes
• Winning Jeopardy

Advanced Data Science Projects

• Predicting Heart Disease
• Credit Card Customer Segmentation
• Predicting Insurance Costs
• Classifying Heart Disease
• Predicting Employee Productivity Using Tree Models
• Optimizing Model Prediction
• Predicting Listing Gains in the Indian IPO Market Using TensorFlow

In the following sections, you'll find detailed instructions for each project. We'll cover the tools you'll use and the skills you'll develop. This structured approach will guide you through key data science techniques across various applications.

1. Profitable App Profiles for the App Store and Google Play Markets

Difficulty Level: Beginner

In this beginner-level data science project, you'll step into the role of a data scientist for a company that builds ad-supported mobile apps. Using Python and Jupyter Notebook, you'll analyze real datasets from the Apple App Store and Google Play Store to identify app profiles that attract the most users and generate the highest revenue. By applying data cleaning techniques, conducting exploratory data analysis, and making data-driven recommendations, you'll develop practical skills essential for entry-level data science positions.

Tools and Technologies

• Jupyter Notebook

Prerequisites

To successfully complete this project, you should be comfortable with Python fundamentals such as:

• Variables, data types, lists, and dictionaries
• Writing functions with arguments, return statements, and control flow
• Using conditional logic and loops for data manipulation
• Working with Jupyter Notebook to write, run, and document code

Step-by-Step Instructions

• Open and explore the App Store and Google Play datasets
• Clean the datasets by removing non-English apps and duplicate entries
• Analyze app genres and categories using frequency tables
• Identify app profiles that attract the most users
• Develop data-driven recommendations for the company's next app development project

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

• Cleaning and preparing real-world datasets for analysis using Python
• Conducting exploratory data analysis to identify trends in app markets
• Applying frequency analysis to derive insights from data
• Translating data findings into actionable business recommendations

Relevant Links and Resources

• Example Solution Code

2. Exploring Hacker News Posts

In this beginner-level data science project, you'll analyze a dataset of submissions to Hacker News, a popular technology-focused news aggregator. Using Python and Jupyter Notebook, you'll explore patterns in post creation times, compare engagement levels between different post types, and identify the best times to post for maximum comments. This project will strengthen your skills in data manipulation, analysis, and interpretation, providing valuable experience for aspiring data scientists.

To successfully complete this project, you should be comfortable with Python concepts for data science such as:

• String manipulation and basic text processing
• Working with dates and times using the datetime module
• Using loops to iterate through data collections
• Basic data analysis techniques like calculating averages and sorting
• Creating and manipulating lists and dictionaries
• Load and explore the Hacker News dataset, focusing on post titles and creation times
• Separate and analyze 'Ask HN' and 'Show HN' posts
• Calculate and compare the average number of comments for different post types
• Determine the relationship between post creation time and comment activity
• Identify the optimal times to post for maximum engagement
• Manipulating strings and datetime objects in Python for data analysis
• Calculating and interpreting averages to compare dataset subgroups
• Identifying time-based patterns in user engagement data
• Translating data insights into practical posting strategies
• Original Hacker News Posts dataset on Kaggle

3. Exploring eBay Car Sales Data

In this beginner-level data science project, you'll analyze a dataset of used car listings from eBay Kleinanzeigen, a classifieds section of the German eBay website. Using Python and pandas, you'll clean the data, explore the included listings, and uncover insights about used car prices, popular brands, and the relationships between various car attributes. This project will strengthen your data cleaning and exploratory data analysis skills, providing valuable experience in working with real-world, messy datasets.

To successfully complete this project, you should be comfortable with pandas fundamentals and have experience with:

• Loading and inspecting data using pandas
• Cleaning column names and handling missing data
• Using pandas to filter, sort, and aggregate data
• Creating basic visualizations with pandas
• Handling data type conversions in pandas
• Load the dataset and perform initial data exploration
• Clean column names and convert data types as necessary
• Analyze the distribution of car prices and registration years
• Explore relationships between brand, price, and vehicle type
• Investigate the impact of car age on pricing
• Cleaning and preparing a real-world dataset using pandas
• Performing exploratory data analysis on a large dataset
• Creating data visualizations to communicate findings effectively
• Deriving actionable insights from used car market data
• Original eBay Kleinanzeigen Dataset on Kaggle

4. Finding Heavy Traffic Indicators on I-94

In this beginner-level data science project, you'll analyze a dataset of westbound traffic on the I-94 Interstate highway between Minneapolis and St. Paul, Minnesota. Using Python and popular data visualization libraries, you'll explore traffic volume patterns to identify indicators of heavy traffic. You'll investigate how factors such as time of day, day of the week, weather conditions, and holidays impact traffic volume. This project will enhance your skills in exploratory data analysis and data visualization, providing valuable experience in deriving actionable insights from real-world time series data.

To successfully complete this project, you should be comfortable with data visualization in Python techniques and have experience with:

• Data manipulation and analysis using pandas
• Creating various plot types (line, bar, scatter) with Matplotlib
• Enhancing visualizations using seaborn
• Interpreting time series data and identifying patterns
• Basic statistical concepts like correlation and distribution
• Load and perform initial exploration of the I-94 traffic dataset
• Visualize traffic volume patterns over time using line plots
• Analyze traffic volume distribution by day of the week and time of day
• Investigate the relationship between weather conditions and traffic volume
• Identify and visualize other factors correlated with heavy traffic
• Creating and interpreting complex data visualizations using Matplotlib and seaborn
• Analyzing time series data to uncover temporal patterns and trends
• Using visual exploration techniques to identify correlations in multivariate data
• Communicating data insights effectively through clear, informative plots
• Original Metro Interstate Traffic Volume Data Set

5. Storytelling Data Visualization on Exchange Rates

In this beginner-level data science project, you'll create a storytelling data visualization about Euro exchange rates against the US Dollar. Using Python and Matplotlib, you'll analyze historical exchange rate data from 1999 to 2021, identifying key trends and events that have shaped the Euro-Dollar relationship. You'll apply data visualization principles to clean data, develop a narrative around exchange rate fluctuations, and create an engaging and informative visual story. This project will strengthen your ability to communicate complex financial data insights effectively through visual storytelling.

To successfully complete this project, you should be familiar with storytelling through data visualization techniques and have experience with:

• Creating and customizing plots with Matplotlib
• Applying design principles to enhance data visualizations
• Working with time series data in Python
• Basic understanding of exchange rates and economic indicators
• Load and explore the Euro-Dollar exchange rate dataset
• Clean the data and calculate rolling averages to smooth out fluctuations
• Identify significant trends and events in the exchange rate history
• Develop a narrative that explains key patterns in the data
• Create a polished line plot that tells your exchange rate story
• Crafting a compelling narrative around complex financial data
• Designing clear, informative visualizations that support your story
• Using Matplotlib to create publication-quality line plots with annotations
• Applying color theory and typography to enhance visual communication
• ECB Euro reference exchange rate: US dollar

6. Clean and Analyze Employee Exit Surveys

In this beginner-level data science project, you'll analyze employee exit surveys from the Department of Education, Training and Employment (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. Using Python and pandas, you'll clean messy data, combine datasets, and uncover insights into resignation patterns. You'll investigate factors such as years of service, age groups, and job dissatisfaction to understand why employees leave. This project offers hands-on experience in data cleaning and exploratory analysis, essential skills for aspiring data analysts.

To successfully complete this project, you should be familiar with data cleaning techniques in Python and have experience with:

• Basic pandas operations for data manipulation
• Handling missing data and data type conversions
• Merging and concatenating DataFrames
• Using string methods in pandas for text data cleaning
• Basic data analysis and aggregation techniques
• Load and explore the DETE and TAFE exit survey datasets
• Clean column names and handle missing values in both datasets
• Standardize and combine the "resignation reasons" columns
• Merge the DETE and TAFE datasets for unified analysis
• Analyze resignation reasons and their correlation with employee characteristics
• Applying data cleaning techniques to prepare messy, real-world datasets
• Combining data from multiple sources using pandas merge and concatenate functions
• Creating new categories from existing data to facilitate analysis
• Conducting exploratory data analysis to uncover trends in employee resignations
• DETE Exit Survey Dataset

7. Star Wars Survey

In this beginner-level data science project, you'll analyze survey data about the Star Wars film franchise. Using Python and pandas, you'll clean and explore data collected by FiveThirtyEight to uncover insights about fans' favorite characters, film rankings, and how opinions vary across different demographic groups. You'll practice essential data cleaning techniques like handling missing values and converting data types, while also conducting basic statistical analysis to reveal trends in Star Wars fandom.

To successfully complete this project, you should be familiar with combining, analyzing, and visualizing data while having experience with:

• Converting data types in pandas DataFrames
• Filtering and sorting data
• Basic data aggregation and analysis techniques
• Load the Star Wars survey data and explore its structure
• Analyze the rankings of Star Wars films among respondents
• Explore viewership and character popularity across different demographics
• Investigate the relationship between fan characteristics and their opinions
• Applying data cleaning techniques to prepare survey data for analysis
• Using pandas to explore and manipulate structured data
• Performing basic statistical analysis on categorical and numerical data
• Interpreting survey results to draw meaningful conclusions about fan preferences
• Original Star Wars Survey Data on GitHub

8. Exploring Financial Data using Nasdaq Data Link API

Difficulty Level: Intermediate

In this beginner-friendly data science project, you'll analyze real-world economic data to uncover market trends. Using Python, you'll interact with the Nasdaq Data Link API to retrieve financial datasets, including stock prices and economic indicators. You'll apply data wrangling techniques to clean and structure the data, then use pandas and Matplotlib to analyze and visualize trends in stock performance and economic metrics. This project provides hands-on experience in working with financial APIs and analyzing market data, skills that are highly valuable in data-driven finance roles.

• requests (for API calls)

To successfully complete this project, you should be familiar with working with APIs and web scraping in Python , and have experience with:

• Making HTTP requests and handling responses using the requests library
• Parsing JSON data in Python
• Data manipulation and analysis using pandas DataFrames
• Creating line plots and other basic visualizations with Matplotlib
• Basic understanding of financial terms and concepts
• Set up authentication for the Nasdaq Data Link API
• Retrieve historical stock price data for a chosen company
• Clean and structure the API response data using pandas
• Analyze stock price trends and calculate key statistics
• Fetch and analyze additional economic indicators
• Create visualizations to illustrate relationships between different financial metrics
• Interacting with financial APIs to retrieve real-time and historical market data
• Cleaning and structuring JSON data for analysis using pandas
• Calculating financial metrics such as returns and moving averages
• Creating informative visualizations of stock performance and economic trends
• Nasdaq Data Link API Documentation

9. Popular Data Science Questions

In this beginner-friendly data science project, you'll analyze data from Data Science Stack Exchange to uncover trends in the data science field. You'll identify the most frequently asked questions, popular technologies, and emerging topics. Using SQL and Python, you'll query a database to extract post data, then use pandas to clean and analyze it. You'll visualize trends over time and across different subject areas, gaining insights into the evolving landscape of data science. This project offers hands-on experience in combining SQL, data analysis, and visualization skills to derive actionable insights from a real-world dataset.

To successfully complete this project, you should be familiar with querying databases with SQL and Python and have experience with:

• Writing SQL queries to extract data from relational databases
• Data cleaning and manipulation using pandas DataFrames
• Basic data analysis techniques like grouping and aggregation
• Creating line plots and bar charts with Matplotlib
• Interpreting trends and patterns in data
• Connect to the Data Science Stack Exchange database and explore its structure
• Write SQL queries to extract data on questions, tags, and view counts
• Use pandas to clean the extracted data and prepare it for analysis
• Analyze the distribution of questions across different tags and topics
• Investigate trends in question popularity and topic relevance over time
• Visualize key findings using Matplotlib to illustrate data science trends
• Extracting specific data from a relational database using SQL queries
• Cleaning and preprocessing text data for analysis using pandas
• Identifying trends and patterns in data science topics over time
• Creating meaningful visualizations to communicate insights about the data science field
• Data Science Stack Exchange Data Explorer

10. Investigating Fandango Movie Ratings

In this beginner-friendly data science project, you'll investigate potential bias in Fandango's movie rating system. Following up on a 2015 analysis that found evidence of inflated ratings, you'll compare 2015 and 2016 movie ratings data to determine if Fandango's system has changed. Using Python, you'll perform statistical analysis to compare rating distributions, calculate summary statistics, and visualize changes in rating patterns. This project will strengthen your skills in data manipulation, statistical analysis, and data visualization while addressing a real-world question of rating integrity.

To successfully complete this project, you should be familiar with fundamental statistics concepts and have experience with:

• Data manipulation using pandas (e.g., loading data, filtering, sorting)
• Calculating and interpreting summary statistics in Python
• Creating and customizing plots with matplotlib
• Comparing distributions using statistical methods
• Interpreting results in the context of the research question
• Load the 2015 and 2016 Fandango movie ratings datasets using pandas
• Clean the data and isolate the samples needed for analysis
• Compare the distribution shapes of 2015 and 2016 ratings using kernel density plots
• Calculate and compare summary statistics for both years
• Analyze the frequency of each rating class (e.g., 4.5 stars, 5 stars) for both years
• Determine if there's evidence of a change in Fandango's rating system
• Conducting a comparative analysis of rating distributions using Python
• Applying statistical techniques to investigate potential bias in ratings
• Creating informative visualizations to illustrate changes in rating patterns
• Drawing and communicating data-driven conclusions about rating system integrity
• Original FiveThirtyEight Article on Fandango Ratings

11. Finding the Best Markets to Advertise In

In this beginner-friendly data science project, you'll analyze survey data from freeCodeCamp to determine the best markets for an e-learning company to advertise its programming courses. Using Python and pandas, you'll explore the demographics of new coders, their locations, and their willingness to pay for courses. You'll clean the data, handle outliers, and use frequency analysis to identify countries with the most potential customers. By the end, you'll provide data-driven recommendations on where the company should focus its advertising efforts to maximize its return on investment.

To successfully complete this project, you should have a solid grasp on how to summarize distributions using measures of central tendency, interpret variance using z-scores , and have experience with:

• Filtering and sorting DataFrames
• Handling missing data and outliers
• Calculating summary statistics (mean, median, mode)
• Creating and manipulating new columns based on existing data
• Load the freeCodeCamp 2017 New Coder Survey data
• Identify and handle missing values in the dataset
• Analyze the distribution of participants across different countries
• Calculate the average amount students are willing to pay for courses by country
• Identify and handle outliers in the monthly spending data
• Determine the top countries based on number of potential customers and their spending power
• Cleaning and preprocessing survey data for analysis using pandas
• Applying frequency analysis to identify key markets
• Handling outliers to ensure accurate calculations of spending potential
• Combining multiple factors to make data-driven business recommendations
• freeCodeCamp 2017 New Coder Survey Results

12. Mobile App for Lottery Addiction

In this beginner-friendly data science project, you'll develop the core logic for a mobile app aimed at helping lottery addicts better understand their chances of winning. Using Python, you'll create functions to calculate probabilities for the 6/49 lottery game, including the chances of winning the big prize, any prize, and the expected return on buying a ticket. You'll also compare lottery odds to real-life situations to provide context. This project will strengthen your skills in probability theory, Python programming, and applying mathematical concepts to real-world problems.

To successfully complete this project, you should be familiar with probability fundamentals and have experience with:

• Writing functions in Python with multiple parameters
• Implementing combinatorics calculations (factorials, combinations)
• Working with control structures (if statements, for loops)
• Performing mathematical operations in Python
• Basic set theory and probability concepts
• Implement the factorial and combinations functions for probability calculations
• Create a function to calculate the probability of winning the big prize in a 6/49 lottery
• Develop a function to calculate the probability of winning any prize
• Design a function to compare lottery odds with real-life event probabilities
• Implement a function to calculate the expected return on buying a lottery ticket
• Implementing complex probability calculations using Python functions
• Translating mathematical concepts into practical programming solutions
• Creating user-friendly outputs to effectively communicate probability concepts
• Applying programming skills to address a real-world social issue

13. Building a Spam Filter with Naive Bayes

In this beginner-friendly data science project, you'll build a spam filter using the multinomial Naive Bayes algorithm. Working with the SMS Spam Collection dataset, you'll implement the algorithm from scratch to classify messages as spam or ham (non-spam). You'll calculate word frequencies, prior probabilities, and conditional probabilities to make predictions. This project will deepen your understanding of probabilistic machine learning algorithms, text classification, and the practical application of Bayesian methods in natural language processing.

To successfully complete this project, you should be familiar with conditional probability and have experience with:

• Python programming, including working with dictionaries and lists
• Understand probability concepts like conditional probability and Bayes' theorem
• Text processing techniques (tokenization, lowercasing)
• Pandas for data manipulation
• Understanding of the Naive Bayes algorithm and its assumptions
• Load and explore the SMS Spam Collection dataset
• Preprocess the text data by tokenizing and cleaning the messages
• Calculate the prior probabilities for spam and ham messages
• Compute word frequencies and conditional probabilities
• Implement the Naive Bayes algorithm to classify messages
• Test the model and evaluate its accuracy on unseen data
• Implementing the multinomial Naive Bayes algorithm from scratch
• Applying Bayesian probability calculations in a real-world context
• Preprocessing text data for machine learning applications
• Evaluating a text classification model's performance
• SMS Spam Collection Dataset

14. Winning Jeopardy

In this beginner-friendly data science project, you'll analyze a dataset of Jeopardy questions to uncover patterns that could give you an edge in the game. Using Python and pandas, you'll explore over 200,000 Jeopardy questions and answers, focusing on identifying terms that appear more often in high-value questions. You'll apply text processing techniques, use the chi-squared test to validate your findings, and develop strategies for maximizing your chances of winning. This project will strengthen your data manipulation skills and introduce you to practical applications of natural language processing and statistical testing.

To successfully complete this project, you should be familiar with intermediate statistics concepts like significance and hypothesis testing with experience in:

• String operations and basic regular expressions in Python
• Implementing the chi-squared test for statistical analysis
• Working with CSV files and handling data type conversions
• Basic natural language processing concepts (e.g., tokenization)
• Load the Jeopardy dataset and perform initial data exploration
• Clean and preprocess the data, including normalizing text and converting dollar values
• Implement a function to find the number of times a term appears in questions
• Create a function to compare the frequency of terms in low-value vs. high-value questions
• Apply the chi-squared test to determine if certain terms are statistically significant
• Analyze the results to develop strategies for Jeopardy success
• Processing and analyzing large text datasets using pandas
• Applying statistical tests to validate hypotheses in data analysis
• Implementing custom functions for text analysis and frequency comparisons
• Deriving actionable insights from complex datasets to inform game strategy
• J! Archive - Fan-created archive of Jeopardy! games and players

15. Predicting Heart Disease

Difficulty Level: Advanced

In this challenging but guided data science project, you'll build a K-Nearest Neighbors (KNN) classifier to predict the risk of heart disease. Using a dataset from the UCI Machine Learning Repository, you'll work with patient features such as age, sex, chest pain type, and cholesterol levels to classify patients as having a high or low risk of heart disease. You'll explore the impact of different features on the prediction, optimize the model's performance, and interpret the results to identify key risk factors. This project will strengthen your skills in data preprocessing, exploratory data analysis, and implementing classification algorithms for healthcare applications.

• scikit-learn

To successfully complete this project, you should be familiar with supervised machine learning in Python and have experience with:

• Implementing machine learning workflows with scikit-learn
• Understanding and interpreting classification metrics (accuracy, precision, recall)
• Feature scaling and preprocessing techniques
• Basic data visualization with Matplotlib
• Load and explore the heart disease dataset from the UCI Machine Learning Repository
• Preprocess the data, including handling missing values and scaling features
• Split the data into training and testing sets
• Implement a KNN classifier and evaluate its initial performance
• Optimize the model by tuning the number of neighbors (k)
• Analyze feature importance and their impact on heart disease prediction
• Interpret the results and summarize key findings for healthcare professionals
• Implementing and optimizing a KNN classifier for medical diagnosis
• Evaluating model performance using various metrics in a healthcare context
• Analyzing feature importance in predicting heart disease risk
• Translating machine learning results into actionable healthcare insights
• UCI Machine Learning Repository: Heart Disease Dataset

16. Credit Card Customer Segmentation

In this challenging but guided data science project, you'll perform customer segmentation for a credit card company using unsupervised learning techniques. You'll analyze customer attributes such as credit limit, purchases, cash advances, and payment behaviors to identify distinct groups of credit card users. Using the K-means clustering algorithm, you'll segment customers based on their spending habits and credit usage patterns. This project will strengthen your skills in data preprocessing, exploratory data analysis, and applying machine learning for deriving actionable business insights in the financial sector.

To successfully complete this project, you should be familiar with unsupervised machine learning in Python and have experience with:

• Implementing K-means clustering with scikit-learn
• Feature scaling and dimensionality reduction techniques
• Creating scatter plots and pair plots with Matplotlib and seaborn
• Interpreting clustering results in a business context
• Load and explore the credit card customer dataset
• Perform exploratory data analysis to understand relationships between customer attributes
• Apply principal component analysis (PCA) for dimensionality reduction
• Implement K-means clustering on the transformed data
• Visualize the clusters using scatter plots of the principal components
• Analyze cluster characteristics to develop customer profiles
• Propose targeted strategies for each customer segment
• Applying K-means clustering to segment customers in the financial sector
• Using PCA for dimensionality reduction in high-dimensional datasets
• Interpreting clustering results to derive meaningful customer profiles
• Translating data-driven insights into actionable marketing strategies
• Credit Card Dataset for Clustering on Kaggle

17. Predicting Insurance Costs

In this challenging but guided data science project, you'll predict patient medical insurance costs using linear regression. Working with a dataset containing features such as age, BMI, number of children, smoking status, and region, you'll develop a model to estimate insurance charges. You'll explore the relationships between these factors and insurance costs, handle categorical variables, and interpret the model's coefficients to understand the impact of each feature. This project will strengthen your skills in regression analysis, feature engineering, and deriving actionable insights in the healthcare insurance domain.

To successfully complete this project, you should be familiar with linear regression modeling in Python and have experience with:

• Implementing linear regression models with scikit-learn
• Handling categorical variables (e.g., one-hot encoding)
• Evaluating regression models using metrics like R-squared and RMSE
• Creating scatter plots and correlation heatmaps with seaborn
• Load and explore the insurance cost dataset
• Perform data preprocessing, including handling categorical variables
• Conduct exploratory data analysis to visualize relationships between features and insurance costs
• Create training/testing sets to build and train a linear regression model using scikit-learn
• Make predictions on the test set and evaluate the model's performance
• Visualize the actual vs. predicted values and residuals
• Implementing end-to-end linear regression analysis for cost prediction
• Handling categorical variables in regression models
• Interpreting regression coefficients to derive business insights
• Evaluating model performance and understanding its limitations in healthcare cost prediction
• Medical Cost Personal Datasets on Kaggle

18. Classifying Heart Disease

In this challenging but guided data science project, you'll work with the Cleveland Clinic Foundation heart disease dataset to develop a logistic regression model for predicting heart disease. You'll analyze features such as age, sex, chest pain type, blood pressure, and cholesterol levels to classify patients as having or not having heart disease. Through this project, you'll gain hands-on experience in data preprocessing, model building, and interpretation of results in a medical context, strengthening your skills in classification techniques and feature analysis.

To successfully complete this project, you should be familiar with logistic regression modeling in Python and have experience with:

• Implementing logistic regression models with scikit-learn
• Evaluating classification models using metrics like accuracy, precision, and recall
• Interpreting model coefficients and odds ratios
• Creating confusion matrices and ROC curves with seaborn and Matplotlib
• Load and explore the Cleveland Clinic Foundation heart disease dataset
• Perform data preprocessing, including handling missing values and encoding categorical variables
• Conduct exploratory data analysis to visualize relationships between features and heart disease presence
• Create training/testing sets to build and train a logistic regression model using scikit-learn
• Visualize the ROC curve and calculate the AUC score
• Summarize findings and discuss the model's potential use in medical diagnosis
• Implementing end-to-end logistic regression analysis for medical diagnosis
• Interpreting odds ratios to understand risk factors for heart disease
• Evaluating classification model performance using various metrics
• Communicating the potential and limitations of machine learning in healthcare

19. Predicting Employee Productivity Using Tree Models

In this challenging but guided data science project, you'll analyze employee productivity in a garment factory using tree-based models. You'll work with a dataset containing factors such as team, targeted productivity, style changes, and working hours to predict actual productivity. By implementing both decision trees and random forests, you'll compare their performance and interpret the results to provide actionable insights for improving workforce efficiency. This project will strengthen your skills in tree-based modeling, feature importance analysis, and applying machine learning to solve real-world business problems in manufacturing.

To successfully complete this project, you should be familiar with decision trees and random forest modeling and have experience with:

• Implementing decision trees and random forests with scikit-learn
• Evaluating regression models using metrics like MSE and R-squared
• Interpreting feature importance in tree-based models
• Creating visualizations of tree structures and feature importance with Matplotlib
• Load and explore the employee productivity dataset
• Perform data preprocessing, including handling categorical variables and scaling numerical features
• Create training/testing sets to build and train a decision tree regressor using scikit-learn
• Visualize the decision tree structure and interpret the rules
• Implement a random forest regressor and compare its performance to the decision tree
• Analyze feature importance to identify key factors affecting productivity
• Fine-tune the random forest model using grid search
• Summarize findings and provide recommendations for improving employee productivity
• Implementing and comparing decision trees and random forests for regression tasks
• Interpreting tree structures to understand decision-making processes in productivity prediction
• Analyzing feature importance to identify key drivers of employee productivity
• Applying hyperparameter tuning techniques to optimize model performance
• UCI Machine Learning Repository: Garment Employee Productivity Dataset

20. Optimizing Model Prediction

In this challenging but guided data science project, you'll work on predicting the extent of damage caused by forest fires using the UCI Machine Learning Repository's Forest Fires dataset. You'll analyze features such as temperature, relative humidity, wind speed, and various fire weather indices to estimate the burned area. Using Python and scikit-learn, you'll apply advanced regression techniques, including feature engineering, cross-validation, and regularization, to build and optimize linear regression models. This project will strengthen your skills in model selection, hyperparameter tuning, and interpreting complex model results in an environmental context.

To successfully complete this project, you should be familiar with optimizing machine learning models and have experience with:

• Implementing and evaluating linear regression models using scikit-learn
• Applying cross-validation techniques to assess model performance
• Understanding and implementing regularization methods (Ridge, Lasso)
• Performing hyperparameter tuning using grid search
• Interpreting model coefficients and performance metrics
• Load and explore the Forest Fires dataset, understanding the features and target variable
• Preprocess the data, handling any missing values and encoding categorical variables
• Perform feature engineering, creating interaction terms and polynomial features
• Implement a baseline linear regression model and evaluate its performance
• Apply k-fold cross-validation to get a more robust estimate of model performance
• Implement Ridge and Lasso regression models to address overfitting
• Use grid search with cross-validation to optimize regularization hyperparameters
• Compare the performance of different models using appropriate metrics (e.g., RMSE, R-squared)
• Interpret the final model, identifying the most important features for predicting fire damage
• Visualize the results and discuss the model's limitations and potential improvements
• Implementing advanced regression techniques to optimize model performance
• Applying cross-validation and regularization to prevent overfitting
• Conducting hyperparameter tuning to find the best model configuration
• Interpreting complex model results in the context of environmental science
• UCI Machine Learning Repository: Forest Fires Dataset

21. Predicting Listing Gains in the Indian IPO Market Using TensorFlow

In this challenging but guided data science project, you'll develop a deep learning model using TensorFlow to predict listing gains in the Indian Initial Public Offering (IPO) market. You'll analyze historical IPO data, including features such as issue price, issue size, subscription rates, and market conditions, to forecast the percentage increase in share price on the day of listing. By implementing a neural network classifier, you'll categorize IPOs into different ranges of listing gains. This project will strengthen your skills in deep learning, financial data analysis, and using TensorFlow for real-world predictive modeling tasks in the finance sector.

To successfully complete this project, you should be familiar with deep learning in TensorFlow and have experience with:

• Building and training neural networks using TensorFlow and Keras
• Preprocessing financial data for machine learning tasks
• Implementing classification models and interpreting their results
• Evaluating model performance using metrics like accuracy and confusion matrices
• Basic understanding of IPOs and stock market dynamics
• Load and explore the Indian IPO dataset using pandas
• Preprocess the data, including handling missing values and encoding categorical variables
• Engineer features relevant to IPO performance prediction
• Split the data into training/testing sets then design a neural network architecture using Keras
• Compile and train the model on the training data
• Evaluate the model's performance on the test set
• Fine-tune the model by adjusting hyperparameters and network architecture
• Analyze feature importance using the trained model
• Visualize the results and interpret the model's predictions in the context of IPO investing
• Implementing deep learning models for financial market prediction using TensorFlow
• Preprocessing and engineering features for IPO performance analysis
• Evaluating and interpreting classification results in the context of IPO investments
• Applying deep learning techniques to solve real-world financial forecasting problems
• Securities and Exchange Board of India (SEBI) IPO Statistics

How to Prepare for a Data Science Job

Landing a data science job requires strategic preparation. Here's what you need to know to stand out in this competitive field:

• Research job postings to understand employer expectations
• Develop relevant skills through structured learning
• Build a portfolio of hands-on projects
• Prepare for interviews and optimize your resume
• Commit to continuous learning

Research Job Postings

Start by understanding what employers are looking for. Check out data science job listings on these platforms:

Steps to Get Job-Ready

Focus on these key areas:

• Skill Development: Enhance your programming, data analysis, and machine learning skills. Consider a structured program like Dataquest's Data Scientist in Python path .
• Hands-On Projects: Apply your skills to real projects. This builds your portfolio of data science projects and demonstrates your abilities to potential employers.
• Put Your Portfolio Online: Showcase your projects online. GitHub is an excellent platform for hosting and sharing your work.

Pick Your Top 3 Data Science Projects

Your projects are concrete evidence of your skills. In applications and interviews, highlight your top 3 data science projects that demonstrate:

• Critical thinking
• Technical proficiency
• Problem-solving abilities

We have a ton of great tips on how to create a project portfolio for data science job applications .

Resume and Interview Preparation

Your resume should clearly outline your project experiences and skills . When getting ready for data science interviews , be prepared to discuss your projects in great detail. Practice explaining your work concisely and clearly.

Job Preparation Advice

Preparing for a data science job can be daunting. If you're feeling overwhelmed:

• Remember that everyone starts somewhere
• Connect with mentors for guidance
• Join the Dataquest community for support and feedback on your data science projects

Continuous Learning

Data science is an evolving field. To stay relevant:

• Keep up with industry trends
• Stay curious and open to new technologies
• Look for ways to apply your skills to real-world problems

Preparing for a data science job involves understanding employer expectations, building relevant skills, creating a strong portfolio, refining your resume, preparing for interviews, addressing challenges, and committing to ongoing learning. With dedication and the right approach, you can position yourself for success in this dynamic field.

Data science projects are key to developing your skills and advancing your data science career. Here's why they matter:

• They provide hands-on experience with real-world problems
• They help you build a portfolio to showcase your abilities
• They boost your confidence in handling complex data challenges

In this post, we've explored 21 beginner-friendly data science project ideas ranging from easier to harder. These projects go beyond just technical skills. They're designed to give you practical experience in solving real-world data problems – a crucial asset for any data science professional.

We encourage you to start with any of these beginner data science projects that interests you. Each one is structured to help you apply your skills to realistic scenarios, preparing you for professional data challenges. While some of these projects use SQL, you'll want to check out our post on 10 Exciting SQL Project Ideas for Beginners for dedicated SQL project ideas to add to your data science portfolio of projects.

Hands-on projects are valuable whether you're new to the field or looking to advance your career. Start building your project portfolio today by selecting from the diverse range of ideas we've shared. It's an important step towards achieving your data science career goals.

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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9 Project Ideas for Your Data Analytics Portfolio

Finding the right data analyst portfolio projects can be tricky, especially when you’re new to the field. You might also think that your data projects need to be especially complex or showy, but that’s not the case. The most important thing is to demonstrate your skills, ideally using a dataset that interests you. And the good news? Data is everywhere—you just need to know where to find it and what to do with it.

A good option for getting experience in all domains is to take a  data analytics program  or course. We offer  a top-rated one here at CareerFoundry.

In this article, I’ll highlight the key elements that your data analytics portfolio should demonstrate. I’ll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping , exploratory analysis , and data visualization .

Table of contents

• What data analytics projects should you include in your portfolio?
• Data scraping project ideas
• Exploratory data analysis project ideas
• Data visualization project ideas
• What’s next?

Ready to get inspired? Let’s go!

1. What data analytics projects should you include in your portfolio?

Data analytics is all about finding insights that inform decision-making. But that’s just the end goal.

As any experienced analyst will tell you, the insights we see as consumers are the result of a great deal of work. In fact, about 80% of all data analytics tasks involve preparing data for analysis . This makes sense when you think about it—after all, our insights are only as good as the quality of our data.

Yes, your portfolio needs to show that you can carry out different types of data analysis . But it also needs to show that you can collect data, clean it, and report your findings in a clear, visual manner. As your skills improve, your portfolio will grow in complexity. As a beginner though, you’ll need to show that you can:

• Scrape the web for data
• Carry out exploratory analyses
• Clean untidy datasets
• Communicate your results using visualizations

If you’re inexperienced, it can help to present each item as a mini-data analyst portfolio project of its own . This makes life easier since you can learn the individual skills in a controlled way.

With that in mind, I’ll keep it nice and simple with some basic ideas, and a few tools you might want to explore to help you along the way.

2. Data scraping project ideas for your portfolio

What is data scraping.

Data scraping is the first step in any data analytics project. It involves pulling data (usually from the web) and compiling it into a usable format. While there’s no shortage of great repositories available online, scraping and cleaning data yourself is a great way to show off your skills.

The process of web scraping can be automated using tools like Parsehub , ScraperAPI , or Octoparse (for non-coders) or by using libraries like Beautiful Soup or Scrapy (for developers). Whichever tool you use, the important thing is to show that you understand how it works and can apply it effectively.

Before scraping a website, be sure that you have permission to do so. If you’re not certain, you can always search for a dataset on a repository site like  Kaggle . If it exists there, it’s a good bet you can go straight to the source and scrape it yourself. Bear in mind though—data scraping can be challenging if you’re mining complex, dynamic websites. We recommend starting with something easy—a mostly-static site. Here are some ideas to get you started.

Data scraping portfolio project ideas

The internet movie database.

A good beginner’s project is to extract data from IMDb. You can collect details about popular TV shows, movie reviews and trivia, the heights and weights of various actors, and so on. This kind of information on IMDb is stored in a consistent format across all its pages, making the task a lot easier. There’s also a lot of potential here for further analysis.

Job portals

Many beginners like scraping data from job portals since they often contain standard data types. You can also find lots of online tutorials explaining how to proceed.

To keep it interesting, why not focus on your local area? Collect job titles, companies, salaries, locations, required skills, and so on. This offers great potential for later visualization, such as graphing skillsets against salaries.

E-commerce sites

Another popular one is to scrape product and pricing data from e-commerce sites. For instance, extract product information about Bluetooth speakers on Amazon, or collect reviews and prices on various tablets and laptops.

Once again, this is relatively straightforward to do, and it’s scalable . This means you can start with a product that has a small number of reviews, and then upscale once you’re comfortable using the algorithms.

For something a bit less conventional, another option is to scrape a site like Reddit. You could search for particular keywords, upvotes, user data, and more. Reddit is a very static website, making the task nice and straightforward. You could even scrape Reddit for useful data analytics advice .

Later, you can carry out interesting exploratory analyses, for instance, to see if there are any correlations between popular posts and particular keywords. Which brings me to our next section…

3. Exploratory data analysis project ideas

What is exploratory data analysis.

The next step in any data analyst’s skillset is the ability to carry out an exploratory data analysis (EDA). An EDA looks at the structure of data, allowing you to determine their patterns and characteristics. They also help you to it up. You can extract important variables, detect outliers and anomalies, and generally test your underlying assumptions.

While this process is one of the most time-consuming tasks for a data analyst, it can also be one of the most rewarding. Later modeling focuses on generating answers to specific questions. An EDA, meanwhile, helps you do one of the most exciting bits—generating those questions in the first place.

Languages like R and Python are often used to carry out these tasks. They have many pre-existing algorithms that you can use to carry out the work for you . The real skill lies in presenting your project and its results. How you decide to do this is up to you, but one popular method is to use an interactive documentation tool like Jupyter Notebook . This lets you capture elements of code, along with explanatory text and visualizations, all in one place. Here are some ideas for your portfolio.

Exploratory data analysis portfolio project ideas

Global suicide rates.

This global suicide rates dataset covers suicide rates in various countries, with additional data including year, gender, age, population, GDP, and more. When carrying out your EDA, ask yourself: What patterns can you see? Are suicides rates climbing or falling in various countries? What variables (such as gender or age) can you find that might correlate to suicide rates?

World Happiness Report

On the other end of the scale, the World Happiness Report tracks six factors to measure happiness across the world’s citizens: life expectancy, economics, social support, absence of corruption, freedom, and generosity. So, which country is the happiest? Which continent? Which factor appears to have the greatest (or smallest) impact on a nation’s happiness? Overall, is happiness increasing or decreasing? Access the happiness data over on Kaggle .

Create your own!

Aside from the two ideas above, you could also use your own datasets . After all, if you’ve already scraped your own data, why not use them? For instance, if you scraped a job portal, which locations or regions offer the best-paid jobs? Which offer the least well-paid ones? Why might that be? Equally, with e-commerce data, you could look at which prices and products offer the best value for money.

Ultimately, whichever dataset you’re using, it should grab your attention. If the information is too complex or don’t interest you, you’re likely to run out of steam before you get very far. Keep in mind what further probing you can do to spot interesting trends or patterns, and to extract the insights you need.

We’ve compiled a list of ten great places to find free datasets for your next project .

4. Data visualization project ideas

What is data visualization.

Scraping, tidying, and analyzing data is one thing. Communicating your findings is another. Our brains don’t like looking at numbers and figures, but they love visuals. This is where the ability to create effective data visualizations comes in.

Good visualizations—whether static or interactive—make a great addition to any data analytics portfolio. Showing that you can create visualizations that are both effective and visually appealing will go a long way towards impressing a potential employer.

Some free visualization tools include Google Charts , Canva Graph Maker , and Tableau Public . Meanwhile, if you want to show off your coding abilities, use a Python library such as Seaborn , or flex your R skills with Shiny . Needless to say, there are many tools available to help you. The one you choose depends on what you’re looking to achieve. Here’s a bit of inspiration…

Data visualization portfolio project ideas

Topical subject matter looks great on any portfolio, and the pandemic is nothing if not topical! What’s more, sites like Kaggle already have thousands of Covid-19 datasets available .

How can you represent the data? Could you use a global heatmap to show where cases have spiked, versus where there are very few? Perhaps you could create two overlapping bar charts to show known infections versus predicted infections. Here’s a handy tutorial to help you visualize Covid-19 data using R, Shiny, and Plotly .

Most followed on Instagram

Whether you’re interested in social media, or celebrity and brand culture, this dataset of the most-followed people on Instagram has great potential for visualization. You could create an interactive bar chart that tracks changes in the most followed accounts over time. Or you could explore whether brand or celebrity accounts are more effective at influencer marketing.

Otherwise, why not find another social media dataset to create a visualization? For instance, data scientist Greg Rafferty’s map of the USA nicely highlights the geographical source of trending topics on Instagram.

Travel data

Another topic that lends itself well to visualization is transport data. There’s a great project by Chen Chen on github , using Python to visualize the top tourist destinations worldwide, and the correlation between inbound/outbound tourists with gross domestic product (GDP).

5. What’s next?

In this post, we’ve explored which skills every beginner needs to demonstrate through their data analytics portfolio project ideas. Regardless of the dataset you’re using, you should be able to demonstrate the following abilities:

• Web scraping —using tools like Parsehub, Beautiful Soup, or Scrapy to extract data from websites (remember: static ones are easier!)
• Exploratory data analysis and data cleaning —manipulating data with tools like R and Python, before drawing some initial insights.
• Data visualization —utilizing tools like Tableau, Shiny, or Plotly to create crisp, compelling dashboards, and visualizations.

Once you’ve mastered the basics, you can start getting more ambitious with your data analytics projects. For example, why not introduce some machine learning projects , like sentiment analysis or predictive analysis? The key thing is to start simple and to remember that a good portfolio needn’t be flashy, just competent.

To further develop your skills, there are loads of online courses designed to set you on the right track. To start with, why not try our free, five-day data analytics short course ?

And, if you’d like to learn more about becoming a data analyst and building your portfolio, check out the following:

• How to build a data analytics portfolio 
• The best data analytics certification programs on the market right now
• These are the most common data analytics interview questions

19 Data Science Project Ideas for Beginners

This article will offer 19 data science project ideas for beginners. Pick one or all of them - whatever looks like the most fun to you.

Data science projects are a great way for beginners to get to grips with some of the very basic data science skills and languages that you'll need to pursue data science as a hobby or a career. Tutorials, lessons, and videos are all great, but projects really act as a stepping stone to getting involved with data science and getting your hands dirty.

Data science projects for beginners are better for learning languages and skills because they're stickier. I can watch a video about learning Python 10,000 times, but I only really start to understand Python when I take a project and do it myself. Data science projects are great because you’ve got much more personal vested interest than just watching an online tutorial. You’re motivated to see something through when you have a stake in the matter.

A good project can be anything from learning how to import a dataset all the way to creating your own website or something even more complex. Projects can be personal, just help you learn; they can serve as a portfolio to prove you know what you're talking about.

This article will offer 19 data science project ideas for beginners. Pick one or all of them - whatever looks like the most fun to you. Let’s jump in.

7 Data Science Project Tutorials for Beginners

These seven data science projects are a mix of videos and articles. They cover various different languages depending on what you're interested in learning. You'll learn how to use APIs, how to run predictions, touch on deep learning, and look at regression.

These seven project tutorials for beginners are hands-on and specific, so they’re perfect if you want to get started but you don't really know where. Pick one you like, see where you’re struggling, and use that to start building a list of other data science skills you can learn.

Project 1: House Prices Regression

During the pandemic, I found myself spending a lot of time on Zillow. I loved looking at all the different houses because they were so rich in data. There are so many different aspects for me to investigate and lose myself in. That strange interest led me to this tutorial which allows you to predict the final price of homes in Ames, Iowa.

Sounds weird, but it's fun.

You can use either R or Python to run through this project. Honestly, it's an ambitious project especially if you're brand-new to coding. But I'm starting with it because I think it speaks to a question a lot of people have – how much are houses worth? Humans are fundamentally curious, and the best data science projects exploit that curiosity to teach you skills.

What I love about this tutorial on Kaggle is that it has a ton of different options to complete it, and these different solutions are shared with the community. Anybody can upload their own code to this, so it's a really good place to learn and copy from other people (which is really one of the best ways of learning how to code).

Get stuck in with predictions, a bit of machine learning, and some regression.

Project 2: Titanic Classification

One of the world’s best-known tragedies is the sinking of the Titanic. There weren't enough lifeboats for everyone on board causing the death of over 1,500 people. If you look at the data though, it seems that some groups of people were more likely to survive than others.

The same website as in the project above, Kaggle, runs this competition. They tried to figure out what factors were most likely to lead to success - socio-economic status, age, gender, and more. Similar to the house prices project, this project has access to the code of many other programmers that you can learn from. They also have their very own tutorial they offer for total beginners. This is really useful for people who are new to Kaggle as well as coding.

At the end, you'll have built a predictive model that answers that question. I recommend Python for this one.

Whether or not you actually join the competition, this is still one of the great data science projects for beginners to investigate.

Project 3: Deep Learning Number Recognition

Did you know computers can see? A lot of the latest interesting data science projects have to do with computer vision. This tutorial is great to teach you the basics of neural networks and classification methods. During the tutorial, your job is to correctly identify digits from a data set of tens of thousands of handwritten images.

This competition/tutorial is also hosted by Kaggle - you can check out some of their own tutorials, or you can just get stuck in with user-submitted code .

In my opinion, this project isn't as interesting as the Titanic or the house prices tutorial, but it'll teach you some of the basics of a very complex subject. Plus, it’s pretty wild that you can teach a computer to see.

Project 4: YouTube comment sentiment analysis

Don't read the comments! ...Unless you're doing a YouTube comment sentiment analysis data science project for beginners.

This tutorial of YouTube comment sentiment analysis is great because it's truly for beginners. The creator of the video tutorial is a beginner at natural language processing, which is the skill you'll be learning in this tutorial. It's a really cool video that's about 14 minutes long, perfect for getting started with NLP. It’s also a great representation that shows how data science projects can run away with you, in a good way.

The video is really funny, and she links to the code in her GitHub . Feel free to get into it yourself!

Project 5: Covid-19 Data Analysis Project

During the pandemic, it felt like things were out of my control. It sounds silly, but one of the ways I grounded myself was just by keeping track of daily numbers. Sometimes it stressed me out, but I found myself looking to data as a way to understand the unimaginable.

The Python Programmer channel had a similar idea. In this tutorial, he teaches you to do Covid-19 data analysis using Python.

This video tutorial is a bit more serious than the previous one, and it goes a little bit more in-depth about how it's done. He also covers the basics of some pretty key Python packages like pandas. It’s a really clear introduction to pandas and Python.

Project 6: Scrape IG comments

There’s so much information on the internet. Most of the tutorials above give you datasets to play with, but sometimes it’s useful to know how to find and use your own data. That’s where knowing how to scrape comes in handy. Plus, maybe you don’t particularly care about YouTube comments or Covid-19 data, but Instagram is really your jam.

The official Instagram API allows you to programmatically access your own comments. But it doesn't like you do that for other people. If you're like me, and you wanted to have a look at posts made by the people get a list of posts with a particular #or scrape the comments of other people's posts. You need something else - a scraper.

This article isn't really a tutorial so much as instructions for your own project, but I love Apify as an Instagram scraping tool. With this, you can grab the data and investigate your own questions. Do certain hashtags get more likes? Do captions elicit more comments? The sky's the limit.

Project 7: YouTube APIs with Python

Speaking of APIs, working with APIs is a necessary skill set for all of the other scientists. When you're choosing a project, make sure at least one of them teaches you to work with APIs to ensure you've covered this critical skill.

This tutorial uses Python to walk you through making an API call to collect video statistics from a channel, and saving it as a pandas dataframe. It also offers you the Python notebook code and additional resources on GitHub.

5 DIY Data Science Project Ideas for Beginners [Unlimited Data Science Project Ideas]

There are practically millions of potential data science projects out there that I've been documented in tutorial and video form. But it's also useful to know how to create your own project. Every other project tutorial out there we'll talk about what other people want to do with - think about what you want to do.

Coming up with my own project was how I ended up getting into Python in the first place. I had a question, I needed an answer, the only way to get it was by analyzing my data with python. Rather than list more individual tutorials, I want to point you to some resources that can help you design your own data science projects from scratch.

Project 8: Tidy Tuesdays

This project relies on the Tidy Tuesday GitHub repo . The great thing about this repo is that every Tuesday, brand-new untidy data is uploaded. The cohort analyzes it, visualizes it, and generally plays around with it. It's a great place to learn from other people and experiment with it yourself.

This repo is best for people who want to learn R (though also good for some Python). It’s also best for basic data science skills, like reading files, doing introductory analysis, visualization, and reporting.

For example, this week’s Tidy Tuesday dataset was from the National Bureau of Economic Research. The way the dataset was structured meant that it was good to learn how to join tables. Maybe you’re interested in checking out the female representation of paper authors. Maybe you want to know about publishing frequency in summer versus winter. Either way, TidyTuesday can help you with some basic data science skills with new data every week. It goes back years, too, so you’ll be able to find something interesting no matter what kind of data you like, and you’ll never run out of data science project ideas.

Project 9: The Pudding

The Pudding does really jazzy visualizations and analysis, usually using JavaScript, Python, or R. TidyTuesday is great for sheer volume, but The Pudding offers some truly weird projects to take on.

Maybe you also are a huge Community fan like me, and you want to know how many times Abed says the word “Cool,” versus Jeff or Annie. Perhaps you love reading Agony Aunt letters and this insight into thirty years of American anxieties via Dear Abby letters intrigues you.

These projects offer a lot of cultural commentaries. They’re more challenging and niche than some others on this list, but they’re gripping and can teach you a lot about visualizations especially. The Pudding offers all their code on their GitHub repo which I encourage you to check out.

Project 10: 538

Sports and politics collide in the 538 blog, meeting in a glorious burst of statistics and math. Here, you can scroll through the articles, spot whatever interests you, and head to the GitHub repo to see the code and analysis behind the findings. From there, you can dive into the data yourself.

One project I had a fun time digging into was Superbowl ads. The original article talked about how Americans love America, animals, and sex (as represented by their frequency in Superbowl ads). I was interested to know whether there were more sexual ads over the years. Find your own question and dive in!

Project 11: NASA

Who didn’t want to be an astronaut when they grew up? Now is (kind of) your opportunity to chase that dream.

NASA’s data isn’t as user-friendly as the three options I listed above. But the quantity (and general awesomeness) of the data on offer here makes it a must for any data science project list. Instead of trying to trawl through their dense literature and databases, I recommend you start with this “ Space Science with Python '' tutorial series. For example, want to know how close the asteroid 1997BQ passed by Earth in May 2020? Now’s your chance to find out.

Project 12: The Tate museum

The Tate museum ( http://shardcore.org/tatedata/ )

Maybe you’re more of an Arts & Humanities buff. Luckily for you, there’s data available for you to create your own data science project too. Look no further than the Tate museum’s data archive . Here, you can find the metadata for over 3,500 artists.

There’s a lot you can do for yourself with that data, but in case you’re already lost wondering where to begin, the Tate helpfully lists example data science projects others have done with access to this data.

7 Skills-Based Data Science Projects

The first section of this blog post dealt with pretty specific tutorials. The second taught you where to look to create your own data science project ideas. This final one will point you in the right direction for skills-based data science project ideas. This is the most relevant for those who are putting together a resume or thinking about applying for a data science job .

Each of these seven steps is worth being its own data science project for beginners, but once you’re ready, you can also use these seven to create a full project for more intermediate/advanced data scientists.

Project 13: Collect data

The very first step in any data science project is worth being a data science project itself: collect data.

Most times, data does not arrive perfectly formed into neat tables in your computer. You have to figure out how to get it from point A to point B in order to do everything else you want.

Turn it into a project and investigate how to collect data using some of the most popular data science languages , like Python and SQL. Here’s a great article tutorial for scraping data using Python.

Project 14: Clean data

The data is in! But it’s messy. Learning how to clean data was one of the biggest letdowns in my Master’s when I was studying bird conservation. I thought I’d be able to get data in and start analyzing right away. Sadly, there were issues: duplicates, N/As, numbers stored as text, and just about every other issue you can think of.

Some folks say cleaning data is 80% of a data scientist’s job. It’s worth knowing how to do.

I did my project using R, so if that’s you, I recommend this tutorial to learn how to load and clean data using R. If you’re a budding Pythonista, this tutorial helped me get to grips with cleaning data with Pandas and NumPy, both very common and useful Python packages.

Project 15: Explore data

Once your data is in and relatively tidy, it’s time for the exciting part: explore your data. This isn’t quite to the level of visualizing or analyzing it. Usually, there’s so much data you’re looking at that it helps to get a feel for what’s actually going on before you begin creating models. Think of this project like dipping your toe in the water to gauge the temperature.

This 2.5 hour video tutorial will teach you to build an exploratory data analysis project completely from scratch. It’s lengthy, and 100% comprehensive.

Project 16: Visualize data

There’s a lot you can do to visualize data, and a lot of data science skill is knowing which kind of visualization best represents the idea you’re trying to communicate. That’s why simply working on data viz is a great data science project idea for beginners.

This Kaggle tutorial is a bit boring but will teach you some of the basics of data visualization. With that knowledge, you can go on and create your own data science visualization project - this time using data that you care about.

Project 17: Regression

Regression is a super important predicting tool used in all avenues of data science. It’s what helps you statistically determine the relationship between X and Y. It’s the very basics of what will become machine learning.

You can create a project that focuses on regression with any dataset that has an X and Y variable. I did this myself with my bird data, predicting whether the size of the bird influenced the survival of the bird. Pick any dataset you like and use a method like Kaggle’s red wine quality data tutorial, linked here .

Project 18: Statistics in general

It’s easy to get caught up in the hype of NLP, ML, AI, DL, and every other data science acronym. But don’t forget data science of all kinds relies on statistics and math. To get the most out of any data science project idea you may have, ensure you have grasped the fundamentals of statistics underpinning the concepts of data science.

I’m cheating a little bit by grouping all these statistical fundamentals under a single subheader, but I recommend KDNuggets’s list of eight basic statistics concepts . From there, find a project that focuses on each of the eight. For instance, take the Tate dataset I linked above and learn about the “central tendency” by figuring out the median paint date of the artwork.

You can use any programming language for this project. I like Python since it’s great for beginners anyway, but R, SQL, JavaScript, or any of the other coding languages can accomplish the same goal.

Project 19: Machine learning

Let’s wrap up this list of data science project ideas for beginners with this one: machine learning. Any data scientist worth their salt knows about machine learning and can successfully use it to predict any number of things. Use what you learned from regression and apply it here.

To create a project that will teach you machine learning, nearly any dataset will do. For example, you can use Uber’s pickup data and ask questions like: does Uber make congestion worse? Alternatively, this tutorial which guides you through making movie recommendations could be a good project to tackle. I recommend using Python due to its TensorFlow package which is built for machine learning specifically.

Bonus Projects

There’s always more, and we could be suggesting projects indefinitely. You can see a lot more data projects on our platform for beginners and more advanced users.

We selected two projects that seem ideal for some more data exploration and regression practice.

For regression, there’s the Website Traffic Analysis data project by Linkfire . It makes you use Python (pandas and SciPy, to be specific) to analyze the website traffic, especially the volume, and distribution of events. From the analysis, you should be able to develop ideas for increasing the links’ click rates.

A relatively simple Predicting Price data project by Haensel AMS is perfect for practicing regression. You’re given seven attributes, and your task is to build the price prediction ML model. You’ll have to analyze data, then define, train, and evaluate the model.

Data science project ideas for beginners are unlimited

If you have an ounce of creativity and curiosity, you can trawl the web to find the data and tutorials you need to create your very own data science projects, no matter what your interest or skill levels. This article should serve as a signpost pointing you to potential options which you can peruse at your leisure.

If you need only one project that’ll help you gain full-stack data science experience, check out our previous article Data Analytics Project Ideas That Will Get You The Job .

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Top 100 Data Science Project Ideas For Final Year

Are you a final year student diving into the world of data science, seeking inspiration for your final project? Look no further! In this blog, we’ll explore a variety of engaging and practical data science project ideas for final year that are perfect for showcasing your skills and creativity. Whether you’re interested in analyzing data trends, building machine learning models, or delving into natural language processing, we’ve got you covered. Let’s dive in!

What is Data Science?

Table of Contents

Data science is a multidisciplinary field that combines various techniques, algorithms, and tools to extract insights and knowledge from structured and unstructured data. At its core, data science involves the use of statistical analysis, machine learning, data mining, and data visualization to uncover patterns, trends, and correlations within datasets.

In simpler terms, data science is about turning raw data into actionable insights. It involves collecting, cleaning, and organizing data, analyzing it to identify meaningful patterns or relationships, and using those insights to make informed decisions or predictions.

Data science encompasses a wide range of applications across industries and domains, including but not limited to:

• Business: Analyzing customer behavior, optimizing marketing strategies, and improving operational efficiency.
• Healthcare: Predicting patient outcomes, diagnosing diseases, and personalized medicine.
• Finance: Fraud detection, risk management, and algorithmic trading.
• Technology: Natural language processing, image recognition, and recommendation systems.
• Environmental Science: Climate modeling, predicting natural disasters, and analyzing environmental data.

In summary, data science is a powerful discipline that leverages data-driven approaches to solve complex problems, drive innovation, and generate value in various fields and industries.

It plays a crucial role in today’s data-driven world, enabling organizations to make better decisions, improve processes, and create new opportunities for growth and development.

How to Select Data Science Project Ideas For Final Year?

Selecting the right data science project idea for your final year is crucial as it can shape your learning experience, showcase your skills to potential employers, and contribute to solving real-world problems. Here’s a step-by-step guide on how to select data science project ideas for your final year:

• Understand Your Interests and Strengths

Reflect on your interests within the field of data science. Are you passionate about healthcare, finance, social media, or environmental issues? Consider your strengths as well. 

Are you proficient in programming languages like Python or R? Do you have experience with statistical analysis, machine learning, or data visualization? Identifying your interests and strengths will help narrow down project ideas that align with your skills and passions.

• Consider the Impact

Think about the impact you want your project to have. Do you aim to address a specific problem or challenge in society, industry, or academia?

Consider the potential beneficiaries of your project and how it can contribute to positive change. Projects with a clear and measurable impact are often more compelling and rewarding.

• Assess Data Availability

Check the availability of relevant datasets for your project idea. Are there publicly available datasets that you can use for analysis? Can you collect data through web scraping, APIs, or surveys?

Ensure that the data you plan to work with is reliable, relevant, and adequately sized to support your analysis and modeling efforts.

• Define Clear Objectives

Clearly define the objectives of your project. What do you aim to accomplish? Are you exploring trends, building predictive models, or developing new algorithms?

Establishing clear objectives will guide your project’s scope, methodology, and evaluation criteria.

• Explore Project Feasibility

Evaluate the feasibility of your project idea given the resources and time constraints of your final year.

Consider factors such as data availability, computational requirements, and the complexity of the techniques you plan to use. Choose a project idea that is challenging yet achievable within your timeframe and resources.

• Seek Inspiration and Guidance

Look for inspiration from existing data science projects, research papers, and industry case studies. Attend workshops, conferences, or webinars related to data science to stay updated on emerging trends and technologies.

Seek guidance from your professors, mentors, or industry professionals who can provide valuable insights and feedback on your project ideas.

• Brainstorm and Refine

Brainstorm multiple project ideas and refine them based on feedback, feasibility, and alignment with your interests and goals.

Consider interdisciplinary approaches that combine data science with other fields such as healthcare, finance, or environmental science. Iterate on your ideas until you find one that excites you and meets the criteria outlined above.

• Plan for Iterative Development

Recognize that data science projects often involve iterative development and refinement.

Plan to iterate on your project as you gather new insights, experiment with different techniques, and incorporate feedback from stakeholders. Embrace the iterative process as an opportunity for continuous learning and improvement.

By following these steps, you can select a data science project idea for your final year that is engaging, impactful, and aligned with your interests and aspirations. Remember to stay curious, persistent, and open to exploring new ideas throughout your project journey.

Exploratory Data Analysis Projects

• Analysis of demographic trends using census data
• Social media sentiment analysis
• Customer segmentation for marketing strategies
• Stock market trend analysis
• Crime rates and patterns in urban areas

Machine Learning Projects

• Healthcare outcome prediction
• Fraud detection in financial transactions
• E-commerce recommendation systems
• Housing price prediction
• Sentiment analysis for product reviews

Natural Language Processing (NLP) Projects

• Text summarization for news articles
• Topic modeling for large text datasets
• Named Entity Recognition (NER) for extracting entities from text
• Social media comment sentiment analysis
• Language translation tools for multilingual communication

Big Data Projects

• IoT data analysis
• Real-time analytics for streaming data
• Recommendation systems using big data platforms
• Social network data analysis
• Predictive maintenance for industrial equipment

Data Visualization Projects

• Interactive COVID-19 dashboard
• Geographic information system (GIS) for spatial data analysis
• Network visualization for social media connections
• Time-series analysis for financial data
• Climate change data visualization

Healthcare Projects

• Disease outbreak prediction
• Patient readmission rate prediction
• Drug effectiveness analysis
• Medical image classification
• Electronic health record analysis

Finance Projects

• Stock price prediction
• Credit risk assessment
• Portfolio optimization
• Fraud detection in banking transactions
• Financial market trend analysis

Marketing Projects

• Customer churn prediction
• Market segmentation analysis
• Brand sentiment analysis
• Ad campaign optimization
• Social media influencer identification

E-commerce Projects

• Product recommendation systems
• Customer lifetime value prediction
• Market basket analysis
• Price elasticity modeling
• User behavior analysis

Education Projects

• Student performance prediction
• Dropout rate analysis
• Personalized learning recommendation systems
• Educational resource allocation optimization
• Student sentiment analysis

Environmental Projects

• Air quality prediction
• Climate change impact analysis
• Wildlife conservation modeling
• Water quality monitoring
• Renewable energy forecasting

Social Media Projects

• Trend detection
• Fake news detection
• Influencer identification
• Social network analysis
• Hashtag sentiment analysis

Retail Projects

• Inventory management optimization
• Demand forecasting
• Customer segmentation for targeted marketing
• Price optimization

Telecommunications Projects

• Network performance optimization
• Fraud detection
• Call volume forecasting
• Subscriber segmentation analysis

Supply Chain Projects

• Inventory optimization
• Supplier risk assessment
• Route optimization
• Supply chain network analysis

Automotive Projects

• Predictive maintenance for vehicles
• Traffic congestion prediction
• Vehicle defect detection
• Autonomous vehicle behavior analysis
• Fleet management optimization

Energy Projects

• Predictive maintenance for equipment
• Energy consumption forecasting
• Renewable energy optimization
• Grid stability analysis
• Demand response optimization

Agriculture Projects

• Crop yield prediction
• Pest detection
• Soil quality analysis
• Irrigation optimization
• Farm management systems

Human Resources Projects

• Employee churn prediction
• Performance appraisal analysis
• Diversity and inclusion analysis
• Recruitment optimization
• Employee sentiment analysis

Travel and Hospitality Projects

• Demand forecasting for hotel bookings
• Customer sentiment analysis for reviews
• Pricing strategy optimization
• Personalized travel recommendations
• Destination popularity prediction

Embarking on data science projects in their final year presents students with an excellent opportunity to apply their skills, gain practical experience, and make a tangible impact.

Whether it’s exploring demographic trends, building predictive models, or visualizing complex datasets, these projects offer a platform for innovation and learning.

By undertaking these data science project ideas for final year, final year students can hone their data science skills and prepare themselves for a successful career in this rapidly evolving field.

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37 Data Analytics Project Ideas and Datasets (2024 UPDATE)

Introduction.

Data analytics projects help you to build a portfolio and land interviews. It is not enough to just do a novel analytics project however, you will also have to market your project to ensure it gets found.

The first step for any data analytics project is to come up with a compelling problem to investigate. Then, you need to find a dataset to analyze the problem. Some of the strongest categories for data analytics project ideas include:

• Beginner Analytics Projects  - For early-career data analysts, beginner projects help you practice new skills.
• Python Analytics Projects - Python allows you to scrape relevant data and perform analysis with pandas dataframes and SciPy libraries.
• Rental and Housing Data Analytics Projects - Housing data is readily available from public sources, or can be simple enough to create your own dataset. Housing is related to many other societal forces, and because we all need some form of it, the topic will always be of interest to many people.
• Sports and NBA Analytics Projects - Sports data can be easily scraped, and by using player and game stats you can analyze strategies and performance.
• Data Visualization Projects - Visualizations allow you to create graphs and charts to tell a story about the data.
• Music Analytics Projects - Contains datasets for music-related data and identifying music trends.
• Economics and Current Trends - From exploring GDPs of respective countries to the spread of the COVID-19 virus, these datasets will allow you to explore a wide variety of time-relevant data.
• Advanced Analytics Projects - For data analysts looking for a stack-filled project.

A data analytics portfolio is a powerful tool for landing an interview. But how can you build one effectively?

Start with a data analytics project and build your portfolio around it. A data analytics project involves taking a dataset and analyzing it in a specific way to showcase results. Not only do they help you build your portfolio, but analytics projects also help you:

• Learn new tools and techniques.
• Work with complex datasets.
• Practice packaging your work and results.
• Prep for a case study and take-home interviews.
• Give you inbound interviews from hiring managers that have read your blog post!

Beginner Data Analytics Projects

Projects are one of the best ways for beginners to practice data science skills, including visualization, data cleaning, and working with tools like Python and pandas.

1. Relax Predicting User Adoption Take-Home

This data analytics take-home assignment, which has been given to data analysts and data scientists at Relax Inc., asks you to dig into user engagement data. Specifically, you’re asked to determine who an “adopted user” is, which is a user who has logged into the product on three separate days in at least one seven-day period.

Once you’ve identified adopted users, you’re asked to surface factors that predict future user adoption.

How you can do it: Jump into the Relax take-home data. This is an intensive data analytics take-home challenge, which the company suggests you spend 12 hours on (although you’re welcome to spend more or less). This is a great project for practicing your data analytics EDA skills, as well as surfacing predictive insights from a dataset.

2. Salary Analysis

Are you in some sort of slump, or do you find the other projects a tad too challenging? Here’s something that’s really easy; this is a salary dataset from Kaggle that is easy to read and clean, and yet still has many dimensions to interpret.

This salary dataset is a good candidate for descriptive analysis , and we can identify which demographics experience reduced or increased salaries. For example, we could explore the salary variations by gender, age, industry, and even years of prior work.

How you can do it: The first step is to grab the dataset from Kaggle. You can either use it as-is and use spreadsheet tools such as Excel to analyze the data, or you can load it into a local SQL server and design a database around the available data. You can then use visualization tools such as Tableau to visualize the data; either through Tableau MySQL Connector, or Tableau’s CSV import feature.

3. Skilledup Messy Product Data Analysis Take-Home

This data analytics take-home from Skilledup, asks participants to perform analysis on a dataset of product details that is formatted inconveniently. This challenge provides an opportunity to show your data cleaning skills, as well as your ability to perform EDA and surface insights from an unfamiliar dataset. Specifically, the assignment asks you to consider one product group, named Books.

Each product in the group is associated with categories. Of course, there are tradeoffs to categorization, and you’re asked to consider these questions:

• Is there redundancy in the categorization?
• How can redundancy be identified and removed?
• Is it possible to reduce the number of categories dramatically by sacrificing relatively few category entries?

How you can do it: You can access this EDA takehome on Interview Query. Open the dataset and perform some EDA to familiarize yourself with the categories. Then, you can begin to consider the questions that are posed.

4. Marketing Analytics Exploratory Data Analysis

This  marketing analytics dataset  on Kaggle includes customer profiles, campaign successes and failures, channel performance, and product preferences. It’s a great tool for diving into marketing analytics, and there are a number of questions you can answer from the data like:

• What factors are significantly related to the number of store purchases?
• Is there a significant relationship between the region the campaign is run in and that campaign’s success?
• How does the U.S. compare to the rest of the world in terms of total purchases?

How you can do it:  This  Kaggle Notebook from user Jennifer Crockett  is a good place to start, and includes quite a few visualizations and analyses.

If you want to take it a step further, there is quite a bit of statistical analysis you can perform as well.

5. UFO Sightings Data Analysis

The UFO Sightings dataset is a fun one to dive into, and it contains data from more than 80,000 sightings over the last 100 years. This is a robust source for a beginner EDA project, and you can create insights into where sightings are reported most frequently sightings in the U.S. vs the rest of the world, and more.

How you can do it:  Jump into the dataset on Kaggle. There are a number of notebooks you can check out with helpful code snippets. If you’re looking for a challenge, one user created an  interactive map with sighting data .

6. Data Cleaning Practice

This  Kaggle Challenge asks you to clean data as well as perform a variety of data cleaning tasks. This is a perfect beginner data analytics project, which will provide hands-on experience performing techniques like handling missing values, scaling and normalization, and parsing dates.

How you can do it:  You can work through this Kaggle Challenge, which includes data. Another option, however, would be to choose your own dataset that needs to be cleaned, and then work through the challenge and adapt the techniques to your own dataset.

Python Data Analytics Projects

Python is a powerful tool for data analysis projects. Whether you are web scraping data - on sites like the New York Times and Craigslist - or you’re conducting EDA on Uber trips, here are three Python data analytics project ideas to try:

7. Enigma Transforming CSV file Take-Home

This take-home challenge - which requires 1-2.5 hours to complete - is a Python script writing task. You’re asked to write a script to transform input CSV data to desired output CSV data. A take-home like this is good practice for the type of Python take-homes that are asked of data analysts, data scientists, and data engineers.

As you work through this practice challenge, focus specifically on the grading criteria, which include:

• How well you solve the problems.
• The logic and approach you take to solving them.
• Your ability to produce, document, and comment on code.
• Ultimately, the ability to write clear and clean scripts for data preparation.

8. Wedding Crunchers

Todd W. Schneider’s  Wedding Crunchers  is a prime example of a data analysis project using Python. Todd  scraped wedding announcements  from the New York Times, performed analysis on the data, and found intriguing tidbits like:

• Distribution of common phrases.
• Average age trends of brides and grooms.
• Demographic trends.

Using the data and his analysis Schneider created a lot of cool visuals, like this one on Ivy League representation in the wedding announcements:

How you can do it:  Follow the example of Wedding Crunchers. Choose a news or media source, scrape titles and text, and analyze the data for trends. Here’s a tutorial for scraping news APIs with Python.

9. Scraping Craigslist

Craigslist is a classic data source for an analytics project, and there is a wide range of things you can analyze. One of the most common listings is for apartments.

Riley Predum created a handy tutorial  that walks you through the steps of using Python and Beautiful Soup to scrape the data to pull apartment listings, and then was able to do some interesting analysis of pricing when segmented by neighborhood and price distributions. When graphed, his analysis looked like this:

How you can do it: Follow the tutorial to learn how to scrape the data using Python. Some analysis ideas: Look at apartment listings for another area, analyze used car prices for your market, or check out what used items sell on Craigslist.

10. Uber Trip Analysis

Here’s a cool project from Aman Kharwal: An  analysis of Uber trip data from NYC.  The project used this  Kaggle dataset from FiveThirtyEight , containing nearly 20 million Uber pickups. There are a lot of angles to analyze this dataset, like popular pickup times or the busiest days of the week.

Here’s a data visualization on pickup times by hour of the day from Aman:

How you can do it:  This is a data analysis project idea if you’re prepping for a case study interview. You can emulate this one, using the dataset on Kaggle, or you can use these similar taxies and  Uber datasets on data.world,  including one for Austin, TX.

11. Twitter Sentiment Analysis

Twitter (now X) is the perfect data source for an analytics project, and you can perform a wide range of analyses based on Twitter datasets. Sentiment analysis projects are great for practicing beginner NLP techniques.

One option would be to measure sentiment in your dataset over time like this:

How you can do it:  This tutorial from Natassha Selvaraj  provides step-by-step instructions to do sentiment analysis on Twitter data. Or see this tutorial from the Twitter developer forum . For data, you can scrape your own or pull some from these free datasets.

12. Home Pricing Predictions

This project has been featured in our list of  Python data science projects . With this project, you can take the classic  California Census dataset , and use it to predict home prices by region, zip code, or details about the house.

Python can be used to produce some stunning visualizations, like this heat map of price by location.

How you can do it: Because this dataset is so well known, there are a lot of helpful tutorials to learn how to predict price in Python. Then, once you’ve learned the technique, you can start practicing it on a variety of datasets like stock prices, used car prices, or airfare.

13. Delivery Time Estimator

This take-home exercise - which requires 5-6 hours to complete - is a two-part task involving both machine learning model development and application engineering. You’re tasked with building a model to predict delivery times based on historical data, followed by writing an application to make predictions using this model. An exercise like this is excellent practice for the type of challenges that are typically given to machine learning engineers and data scientists.

As you work through this exercise, focus specifically on the evaluation criteria, which include:

• The performance of your model on the test data set.
• The feature engineering choices and data processing techniques you employ.
• The clarity and thoroughness of your explanations and write-up.
• Your ability to write modular, well-documented, and production-ready code for the prediction application.

14. Trucking in High Winds

This take-home exercise - which is intended to take 2-3 hours to complete - is focused on estimating the mean distance to failure for wind-induced rollover events on a specified route. You’re asked to analyze historical weather data to assess the frequency of high wind events and to use this information to estimate the risk of rollover incidents. A task like this is good practice for the type of data-driven safety analyses that are relevant to data science roles in the logistics and transportation industry.

Rental and Housing Data Analytics Project Ideas

There’s a ton of accessible housing data online, e.g. sites like Zillow and Airbnb, and these datasets are perfect for analytics and EDA projects.

If you’re interested in price trends in housing, market predictions, or just want to analyze the average home prices for a specific city or state, jump into these projects:

15. Airbnb Data Analytics Take-Home Assignment

• Overview:  Analyze the provided data and make product recommendations to help increase bookings in Rio de Janeiro.
• Time Required:  6 hours
• Skills Tested:  Analytics, EDA, growth marketing, data visualization
• Deliverable:  Summarize your recommendations in response to the questions above in a Jupyter Notebook intended for the Head of Product and VP of Operations (who is not technical).

This take-home is a classic product case study. You have booking data for Rio de Janeiro, and you must define metrics for analyzing matching performance and make recommendations to help increase the number of bookings.

This take-home includes grading criteria, which can help direct your work. Assignments are judged on the following:

• Analytical approach and clarity of visualizations.
• Your data sense and decision-making, as well as the reproducibility of the analysis.
• Strength of your recommendations
• Your ability to communicate insights in your presentation.
• Your ability to follow directions.

16. Zillow Housing Prices

Check out  Zillow’s free datasets.  The Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.

Here’s an  analytics project based in R  that might give you some direction. The author analyzes Zillow data for Seattle, looking at things like the age of inventory (days since listing), % of homes that sell for a loss or gain, and list price vs. sale price for homes in the region:

How you can do it:  There are a ton of different ways you can use the Zillow dataset. Examine listings by region, explore individual list price vs. sale price, or take a look at the average sale price over the average list price by city.

17. Inside Airbnb

On  Inside Airbnb , you’ll find data from Airbnb that has been analyzed, cleaned, and aggregated. There is data for dozens of cities around the world, including number of listings, calendars for listings, and reviews for listings.

Agratama Arfiano has extensively examined Airbnb data for Singapore. There are a lot of different analyses you can do, including finding the number of listings by host or listings by neighborhood. Arfiano has produced some really striking visualizations for this project, including the following:

How you can do it:  Download the data from Inside Airbnb, then choose a city for analysis. You can look at the price, listings by area, listings by the host, the average number of days a listing is rented, and much more.

18. Car Rentals

Have you ever wondered which cars are the most rented? Curious how fares change by make and model? Check out the Cornell Car Rental Dataset on Kaggle. Kushlesh Kumar created the dataset, which features records on 6,000+ rental cars. There are a lot of questions you can answer with this dataset: Fares by make and model, fares by city, inventory by city, and much more. Here’s a cool visualization from Kushlesh:

How you can do it: Using the dataset, you could analyze rental cars by make and model, a particular location, or analyze specific car manufacturers. Another option: Try a similar project with these datasets:  Cash for Clunkers cars ,  Carvana sales data or used cars on eBay .

19. Analyzing NYC Property Sales

This  real estate dataset  shows every property that sold in New York City between September 2016 and September 2017. You can use this data (or a similar dataset you create) for a number of projects, including EDA, price predictions, regression analysis, and data cleaning.

A beginner analytics project you can try with this data would be a missing values analysis project like:

How you can do it: There are a ton of  helpful Kaggle notebooks  you can browse to learn how to: perform price predictions, do data cleaning tasks, or do some interesting EDA with this dataset.

Sports and NBA Data Analytics Projects

Sports data analytics projects are fun if you’re a fan, and also, because there are quite a few free data sources available like Pro-Football-Reference and Basketball-Reference. These sources allow you to pull a wide range of statistics and build your own unique dataset to investigate a problem.

20. NBA Data Analytics Project

Check out this  NBA data analytics project  from Jay at Interview Query. Jay analyzed data from  Basketball Reference  to determine the impact of the 2-for-1 play in the NBA. The idea: In basketball, the 2-for-1 play refers to an end-of-quarter strategy where a team aims to shoot the ball with between 25 and 36 seconds on the clock. That way the team that shoots first has time for an additional play while the opposing team only gets one response. (You can see the  source code on GitHub).

The main metric he was looking for was the differential gain between the score just before the 2-for-1 shot and the score at the end of the quarter. Here’s a look at a differential gain:

How you can do it: Read this tutorial on  scraping Basketball Reference data . You can analyze in-game statistics, career statistics, playoff performance, and much more. An idea could be to analyze a player’s high school ranking  vs. their success in the NBA. Or you could visualize a player’s career.

21. Olympic Medals Analysis

This is a great dataset for a sports analytics project. Featuring 35,000 medals awarded since 1896, there is plenty of data to analyze, and it’s useful for identifying performance trends by country and sport. Here’s a visualization from Didem Erkan :

How you can do it: Check out the  Olympics medals dataset . Angles you might take for analysis include: Medal count by country (as in this visualization ), medal trends by country, e.g., how U.S. performance evolved during the 1900s, or even grouping countries by region to see how fortunes have risen or faded over time.

22. Soccer Power Rankings

FiveThirtyEight is a wonderful source of sports data; they have NBA datasets, as well as data for the NFL and NHL. The site uses its Soccer Power Index (SPI) ratings for predictions and forecasts, but it’s also a good source for analysis and analytics projects. To get started, check out Gideon Karasek’s breakdown of  working with the SPI data .

How you can do it:  Check out the  SPI data . Questions you might try to answer include: How has a team’s SPI changed over time, comparisons of SPI amongst various soccer leagues, and goals scored vs. goals predicted?

23. Home Field Advantage Analysis

Does home-field advantage matter in the NFL? Can you quantify how much it matters? First, gather data from  Pro-Football-Reference.com . Then you can perform a simple linear regression model to measure the impact.

There are a ton of projects you can do with NFL data. One would be to  determine WR rankings, based on season performance .

How you can do it:  See this Github repository on performing a  linear regression to quantify home field advantage .

24. Daily Fantasy Sports

Creating a model to perform in daily fantasy sports requires you to:

• Predict which players will perform best based on matchups, locations, and other indicators.
• Build a roster based on a “salary cap” budget.
• Determine which players will have the top ROI during the given week.

If you’re interested in fantasy football, basketball, or baseball, this would be a strong project.

How you can do it: Check out the  Daily Fantasy Data Science course , if you want a step-by-step look.

Data Visualization Projects

All of the datasets we’ve mentioned would make for amazing data visualization projects. To cap things off we are highlighting three more ideas for you to use as inspiration that potentially draws from your own experiences or interests!

25. Supercell Data Scientist Pre-Test

This is a classic SQL/data analytics take-home. You’re asked to explore, analyze, visualize and model Supercell’s revenue data. Specifically, the dataset contains user data and transactions tied to user accounts.

You must answer questions about the data, like which countries produce the most revenue. Then, you’re asked to create a visualization of the data, as well as apply machine learning techniques to it.

26. Visualizing Pollution

This project by Jamie Kettle visualizes plastic pollution by country, and it does a scarily good job of showing just how much plastic waste enters the ocean each year. Take a look for inspiration:

How you can do it: There are dozens of pollution datasets on data.world . Choose one and create a visualization that shows the true impact of pollution on our natural environments.

27. Visualizing Top Movies

There are a ton of movie and media datasets on Kaggle:  The Movie Database 5000 ,  Netflix Movies and TV Shows ,  Box Office Mojo data , etc. And just like their big-screen debuts, movie data makes for fantastic visualizations.

Take a look at this  visualization of the Top 100 movies by Katie Silver , which features top movies based on box office gross and the Oscars each received:

How you can do it: Take a Kaggle movie dataset, and create a visualization that shows one of the following: gross earnings vs. average IMDB rating, Netflix shows by rating, or visualization of top movies by the studio.

28. Gender Pay Gap Analysis

Salary is a subject everyone is interested in, and it makes it a relevant subject for visualization. One idea: Take this dataset from the  U.S. Bureau of Labor Statistics , and create a visualization looking at the gap in pay by industry.

You can see an example of a gender pay gap visualization on InformationIsBeautiful.net:

How you can do it: You can re-create the gender pay visualization, and add your own spin. Or use salary data to visualize, fields with the fastest growing salaries, salary differences by cities, or  data science salaries by the company .

29. Visualize Your Favorite Book

Books are full of data, and you can create some really amazing visualizations using the patterns from them. Take a look at this project by Hanna Piotrowska, turning an  Italo Calvo book into cool visualizations . The project features visualizations of word distributions, themes and motifs by chapter, and a visualization of the distribution of themes throughout the book:

How you can do it: This  Shakespeare dataset , which features all of the lines from his plays, would be ripe for recreating this type of project. Another option: Create a visualization of your favorite Star Wars script.

Music Analytics Projects

If you’re a music fan, music analytics projects are a good way to jumpstart your portfolio. Of course, analyzing music through digital signal processing is out of our scope, so the best way to go around music-related projects is through exploring trends and charts. Here are some resources that you may use.

30. Popular Music Analysis

Here’s one way to analyze music features without explicit feature extraction. This dataset from Kaggle contains a list of popular music from the 1960s. A feature of this dataset is that it is currently being maintained. Here are a few approaches you can use.

How you can do it: You can grab this dataset from Kaggle. This dataset has classifications for popularity, release date, album name, and even genre. You can also use pre-extracted features such as time signature, liveness, valence, acoustic-ness, and even tempo.

Load this dataset into a Pandas DataFrame and do your appropriate processes there. You can analyze how the features move over time (i.e., did songs over time get a bit more mellow, livelier, or louder), or you can even explore the rise and fall of artists over time.

31. KPOP Melon Music Charts Analysis

If you’re interested in creating a KPOP-related analytics project, here’s one for you. While this is not a dataset, what we have here is a data source that scrapes data from the Melon charts and shows you the top 100 songs in the weekly, daily, rising, monthly, and LIVE charts.

How you can do it: The problem with this data source is that it is scraped, so gathering previous data might be a bit problematic. In order to do historical analysis, you will need to compile and store the data yourself.

So for this approach, we will prefer a locally hosted infrastructure. Knowing how to use cloud services to automate and store data might introduce additional layers of complexity for you to show off to a recruiter. Here’s a local approach to conducting this project.

The first step is to decide which database solution to use. We recommend XAMPP’s toolkit with MySQL Server and PHPMyAdmin as it provides an easy-to-use frontend while also providing a query builder that allows you to construct table schemas, so learning DDL (Data Definition Language) is not as much of a necessity.

The second step is to create a Python script that scrapes data from Melon’s music charts. Thankfully, we have a module that scrapes data from the charts. First, install the melonapi module. Then, you can gather the data and store it in your database. Here’s a step-by-step guide to loading the data from the site.

Of course, running this script over a period of time manually opens the door to human forgetfulness or boredom. To avoid this, you can use an automation service to automate your processes. For Windows systems, you can use the built-in Windows Task Scheduler. If you’re using Mac, you can use Automator.

When you have the appropriate data, you can then perform analytics, such as examining how songs move over time, classifying songs by album, and so on.

Economic and Current Trends Analytics Projects

One of the most valuable analytics projects is those that delve into economic and current trends. These projects, which make use of data from financial market trends, public demographic data, and social media behavior, are powerful tools not only for businesses and policymakers but also for individuals who aim to better understand the world around them.

When discussing current trends, COVID-19 is a significant phenomenon that continues to profoundly impact the status quo. An in-depth analysis of COVID-19 datasets can provide valuable insights into public health, global economies, and societal behavior.

How you can do it: These datasets, readily available for download, focus on different geographical areas. Here are a few:

• EU COVID-19 Dataset - dataset from the European Centre for Disease Prevention and Control, contains COVID-19 data for EU territories.
• US COVID-19 Dataset - US COVID-19 data provided by the New York Times. However, data might be outdated.
• Mexico COVID-19 Dataset - A COVID-19 dataset provided by the Mexican government.

These datasets provide opportunities to develop predictive algorithms and to create visualizations depicting the virus’s spread over time. Despite COVID-19 being less deadly today, it has become more contagious , and insights derived from these datasets can be crucial for understanding and combating future pandemics. For instance, a time-series analysis could identify key periods of infection rates’ acceleration and slow-down, highlighting effective and ineffective public health measures.

32. News Media Dataset

The News Media Dataset provides valuable information about the top 43 English media channels on YouTube, including each of their top 50 videos. This dataset, although limited in its scope, can offer intriguing insights into viewer preferences and trends in news consumption.

How you can do it: Grab the dataset from Kaggle and use the dataset which contains the top 50 viewed videos per channel. There are a lot of insights you can gain here, such as using a basic sentiment analysis tool to determine whether the top-performing headlines were positive or negative.

For sentiment analysis, you don’t necessarily need to train a model. You can load the CSV file and loop through all the tags. Use the TextBlob module to conduct sentiment analysis. Here’s how you can go about doing it:

Then, by using the subjectivity and polarity metrics, you can create visualizations that reflect your findings.

33. The Big Mac Index Analytics

The Big Mac Index offers an intriguing approach to comparing purchasing power parity (PPP) between different countries. The index shows how the U.S. dollar compares to other currencies, through a standardized, identical product, the McDonald’s Big Mac. The dataset, provided by Andrii Samoshyn, contains a lot of missing data, offering a real-world exercise in data cleaning. The data goes back to April 2000 up until January 2020.

How you can do it: You can download the dataset from Kaggle here . One common strategy for handling missing data is by using measures of central tendency like mean or median to fill in gaps. More advanced techniques, such as regression imputation, could also be applicable depending on the nature of the missing data.

Using this cleaned dataset, you can compare values over time or between regions. Introducing a “geographical proximity” column could provide additional layers of analysis, allowing comparisons between neighboring countries. Machine Learning techniques like clustering or classification could reveal novel groupings or patterns within the data, providing a richer interpretation of global economic trends.

When conducting these analyses, it’s important to keep in mind methods for evaluating the effectiveness of your work. This might involve statistical tests for significance, accuracy measures for predictive models, or even visual inspection of plotted data to ensure trends and patterns have been accurately captured. Remember, any analytics project is incomplete without a robust method of evaluation.

34. Global Country Information Dataset

This dataset offers a wealth of information about various countries, encompassing factors such as population density, birth rate, land area, agricultural land, Consumer Price Index (CPI), Gross Domestic Product (GDP), and much more. This data provides ample opportunity for comprehensive analysis and correlation studies among different aspects of countries.

How you can do it : Download this dataset from Kaggle. This dataset includes diverse attributes, ranging from economic to geographic factors, creating an array of opportunities for analysis. Here are some project ideas:

• Correlation Analysis: Investigate the correlations between different attributes, such as GDP and education enrollment, population density and CO2 emissions, birth rate, and life expectancy. You can use libraries like pandas and seaborn in Python for these tasks.
• Geospatial Analysis: With latitude and longitude data available, you could visualize data on a world map to understand global patterns better. Libraries such as geopandas and folium can be helpful here.
• Predictive Modeling: Try to predict an attribute based on others. For instance, could you predict a country’s GDP based on factors like population, education enrollment, and CO2 emissions?
• Cluster Analysis: Group countries based on various features to identify patterns. Are there groups of countries with similar characteristics, and if so, why?

Remember to perform EDA before diving into modeling or advanced analysis, as this will help you understand your data better and could reveal insights or trends to explore further.

35. College Rankings and Tuition Costs Dataset

This dataset offers valuable information regarding various universities, including their rankings and tuition fees. It allows for a comprehensive analysis of the relationship between a university’s prestige, represented by its ranking, and its cost.

How you can do it: First, download the dataset from Kaggle . You can then use Python’s pandas for data handling, and matplotlib or seaborn for visualization.

Possible analyses include exploring the correlation between college rankings and tuition costs, comparing tuition costs of private versus public universities, and studying trends in tuition costs over time. For a more advanced task, try predicting college rankings based on tuition and other variables.

Advanced Data Analytics Project

Ready to take your data skills to the next level? Advanced projects are a way to do just that. They’re all about handling larger datasets, really digging into data cleaning and preprocessing, and getting your hands dirty with a range of tech stacks. It’s a two-in-one deal – you’ll dip your toes inside the roles of both a data engineer and a data scientist. Here are some project ideas to consider.

36. Analyzing Google Trends Data

Google Trends, a free service provided by Google, can serve as a treasure trove for data analysts, offering insights into popular trends worldwide. But there’s a hitch. Google Trends does not support any official API, making direct data acquisition a bit challenging. However, there’s a workaround — web scraping. This guide will walk you through the process of using a Python module for scraping Google Trends data.

How you can do it: Of course, we would not want to implement a web scraper ourselves. Simply put, it’s too much work. For this project, we will utilize a Python module that will help us scrape the data. Let’s view an example:

This code should print out the data in the following format:

You should use an automation service to automate scraping at least once per hour (see: KPOP Melon Music Charts Analysis) . Then, you should store the results in a CSV file that you can query later. There are many points of analysis, such as keyword rankings, website rankings for articles, and more.

Taking it a step further:

If you want to make an even more robust project that’s bound to wow your recruiters, here are some ideas to make the scraping process easier to maintain, albeit with a higher difficulty in setting up.

The first problem in our previous approach is the hardware issue. Simply put, the automation service we used earlier is moot if our device is off or if it was not instantiated during device startup. To solve this, we can utilize the cloud.

Using a function service (i.e., GCP Cloud Functions, AWS Lambda), you can execute Python scripts. Now, you will need to orchestrate this service, and you can use a Pub/Sub service such as GCP Pub/Sub and AWS SNS. These will alert your cloud functions to run, and you can modify the Pub/Sub service to run at a specified time gap.

Then, when your script successfully scrapes the data, you will need a SQL server instance. The flavor of SQL does not really matter, but you can use the available databases provided by your cloud provider. For example, AWS offers RDS, while GCP offers Cloud SQL.

Once your data is pulled together, you can then start analyzing your data and employing analysis techniques to visualize and interpret data.

37. New York Times (NYT) Movie Reviews Sentiment Analysis

Sentiment Analysis is a critical tool in gauging public opinion and emotional responses towards various subjects, and in this case, movies. With a substantial number of movie reviews published daily in well-circulated publications like the NYT, proper sentiment analysis can provide valuable insights into the perceived quality of films and their reception among critics.

How you can do it: As a data source, NYT has an API service that allows you to query their databases. Create an account at this link and enable the ‘Movie Reviews’ service. Then, using your API key, you can start querying using the following script:

The query looks up the titles and returns movie reviews matching those in the query. You can then use the review summaries to do sentiment analysis.

Other NY Times APIs you can explore include the Most Popular API , and the Top Stories API .

More Analytics Project Resources

If you are still looking for inspiration, see our compiled list of free datasets which features sites to search for free data, datasets for EDA projects and visualizations, as well as datasets for machine learning projects.

You should also read our guide on the data analyst career path , how to become a data analyst without a degree , how to build a data science project from scratch and list of 30 data science project ideas .

You can also check out our blog for more resources like:

How to Get a Data Science Internship

How Hard Is It to Get a Google Internship?

Highest Paying Data Science Jobs

Best 52 Data Science Project Ideas For Final Year

Are you interested in diving into the world of data science and machine learning? Well, you’re in the right place! Data science is a fascinating field that combines mathematics, statistics, and programming to extract meaningful insights from data. To get started on your data science journey, you’ll need some project ideas to practice your skills. In this blog, we’ll present 52 data science project ideas, with explanations for the first 10, to help you get started on your data-driven adventure.

What is Data Science?

Table of Contents

Data science is like a detective for data. It’s a way of using math, statistics, and computers to find valuable information hidden in big piles of data. Think of it as sorting through a jigsaw puzzle without knowing what the final picture looks like. Data scientists collect, clean, and analyze data to discover patterns, make predictions, and solve problems. They help businesses make smart decisions, like suggesting products you might like or finding ways to reduce costs. Data science is all about turning data into knowledge that can guide important choices in the world of business, science, and beyond.

10 Data Science Project Ideas For Final Year

1. predictive sales analysis.

Build a model that predicts future sales based on historical data. This project can help businesses optimize inventory and staffing.

2. Sentiment Analysis on Social Media Posts

Analyze Twitter or Reddit data to determine public sentiment about a specific topic, brand, or event.

3. Movie Recommendation System

Build a system that gives movie suggestions to users by looking at what they like and what they’ve watched before.

4. Credit Card Fraud Detection

Develop a model to identify fraudulent credit card transactions, helping banks and customers prevent financial loss.

5. Natural Language Processing (NLP) Chatbot

Build a chatbot that can engage in conversations, answer questions, and perform simple tasks using NLP techniques.

6. Image Classification

Train a model to classify images into predefined categories, like cats vs. dogs or handwritten digits recognition.

7. Housing Price Prediction

Make a tool that guesses how much a house costs in one place by looking at things like how big it is, how many bedrooms it has, and what neighborhood it’s in.

8. Customer Churn Analysis

Analyze customer behavior data to predict and reduce customer churn for businesses like subscription services.

9. Text Summarization

Create a text summarization tool that can automatically generate concise summaries of long articles or documents.

10. Anomaly Detection

Detect anomalies in time-series data, such as network traffic or equipment sensor readings, to identify unusual patterns or issues.

42 Data Science Project Ideas For Final Year

Now that you have a solid understanding of the first 10 data science project ideas, here are the names of the remaining 42 projects:

• Social Network Analysis
• Stock Price Prediction
• Email Spam Detection
• Language Translation Tool
• Customer Segmentation
• Weather Forecasting
• Healthcare Analytics
• Music Genre Classification
• E-commerce Product Recommendation
• Predictive Maintenance for Machinery
• Personality Prediction from Text
• Restaurant Reviews Sentiment Analysis
• Fraud Detection in Insurance Claims
• Image Style Transfer
• Predicting Disease Outbreaks
• Earnings Call Analysis
• Sports Analytics
• Traffic Congestion Prediction
• Employee Attrition Prediction
• Game Recommendation System
• News Topic Modeling
• Customer Lifetime Value Prediction
• Autonomous Drone Navigation
• Food Recipe Generator
• Movie Script Generation
• Fashion Style Recognition
• Energy Consumption Forecasting
• Environmental Pollution Monitoring
• Object Detection in Images
• Customer Support Chatbot
• Predictive Healthcare Diagnostics
• Vehicle License Plate Recognition
• Social Media Influence Analysis
• Image Super-Resolution
• Cybersecurity Threat Detection
• Demand Forecasting for Retail
• Stock Market Sentiment Analysis
• Music Lyrics Generation
• Voice Assistant for Data Analysis
• Political Opinion Mining
• Wildlife Species Identification
• Education Recommender System

Data science is an exciting field with endless possibilities. We’ve shared 52 data science project ideas to help you embark on your data science journey. The first 10 projects, from sales predictions to anomaly detection, offer a solid foundation to hone your skills.

As you explore these projects, remember that learning by doing is key. Start with projects that match your current skill level and gradually tackle more complex ones. Whether you’re interested in finance, healthcare, entertainment, or any other domain, there’s a data science project waiting for you.

By working on these projects, you’ll gain hands-on experience, build a portfolio, and develop the problem-solving skills crucial for a successful data science career. So, pick a project, gather your data, and start analyzing! With dedication and practice, you’ll be well on your way to becoming a proficient data scientist and making a meaningful impact with your data-driven insights.

Frequently Asked Questions

How can i start working on a data science project as a beginner .

Start with simple projects and learn from online tutorials. Python is a good language to begin with.

What’s the importance of data science in today’s world? 

Data science helps make informed decisions in various fields, from business to healthcare, by uncovering insights hidden in data.

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Top 10 Data Visualization Project Ideas 2024

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

The Importance of Data Visualization

What is data visualization and why is it important?

Data visualization is the art of providing insights with the aid of some type of visual representation, such as charts, graphs, or more complex forms of visualizations like dashboards. Usually, the process involves various data visualization software – top data visualization tools such as Tableau, Power BI, or Python, and R on the programming end.

Investing time in learning data visualization techniques is worthwhile, as data visualization is becoming one of the most sought out fields in data science overall. Moreover, excellent data visualization skills are high-in-demand across a myriad of businesses and industries and open the door to many rewarding career opportunities.

With that in mind, we dedicate this post to some of the classic data visualizations combined with inspirational data visualization project ideas. Data is beautiful and invaluable when presented the right way and we believe the examples we listed below will come in handy in your own practice.

Ready-Made Data Visualization Projects

Before getting into the list of project ideas, we want to highlight the ready-made data visualization projects available on our platform.

With free options and advanced projects as part of your 365 subscription, you can access a wide array of projects that cater to different levels of expertise—from beginners to advanced practitioners looking to further hone their skills.

Using these projects lets you address real-world problems immediately without the stress of creating the project and hunting for data.

Our projects span multiple fields, allowing you to apply your data visualization skills in such areas as music, real estate, and beyond.

These projects will enhance your skills and serve as standout additions to your portfolio. Showcasing your data visualization projects to potential employers can demonstrate your practical skills, creativity, and ability to derive meaningful insights from data.

Consider the following projects we’ve prepared for you.

• Newsfeed Analysis in Tableau (beginner, free )
• Career Track Analysis with SQL and Tableau (beginner, free )
• Music Genre Classification with PCA and Logistic Regression (intermediate)
• Checkout Flow Optimization Analysis with SQL and Tableau (intermediate)
• Student Onboarding Analysis in Tableau (intermediate)
• Customer Engagement Analysis with SQL and Tableau (intermediate)
• Housing Market Data Analysis in R (intermediate)
• Real Estate Market Analysis with Python (advanced)

Hands-on experience is one of the most effective ways to learn and grow. That's why our projects are designed to challenge you and stimulate your creativity while providing practical experience in data visualization. No matter where you are on your data science journey, these projects are a valuable resource to help you progress.

Now, let’s move on to some more data science project ideas that you can prepare yourself.

Top 10 Data Visualization Project Ideas

In this Top 10, you will find the staples in data visualization and ideas on how to use them in different projects. You can use the table of contents to jump directly to the ones that interest you most or just scroll down to absorb all dataviz ideas from first to last.

Table of Contents

• Bar Chart Data Visualization Project ideas
• Time Series Data Visualization Project Ideas
• Box Plot Data Visualization Project Ideas
• Word Cloud Data Visualization Project Ideas
• Map Data Visualization Project Ideas
• Graph Network Data Visualization Project Ideas
• Race Chart Data Visualization Project Ideas
• Correlogram Data Visualization Project Ideas
• Dendrogram Data Visualization Project Ideas
• Heatmap Data Visualization Project Ideas

1. Bar Chart Data Visualization Project Ideas

Any data visualization journey starts with the bar chart.

So, to answer the question we posed at the start “What is data visualization?”: in the majority of cases, the answer is the bar chart. It’s one of the most popular data visualization examples you’ll ever come across because it is truly versatile, intuitive, and clear as a visualization.

There is no shortage of available options here. However, our suggestion is plotting the flight delays values , as suggested in this Kaggle tutorial :

2. Time Series Data Visualization Project Ideas

Time series data is one of the staples in data visualization. So, chances are, no matter what field you’re working in, at one point or another you’ll face a project where you’ll have to display data with time series elements.

For this type of data, it is crucial to make sure the date features in your data are converted into date type format . No matter what your go-to data visualization tools are: Tableau, Python, R, or Excel, the conversion step is crucial to ensure your data is plotted correctly.

That said, here’s a great project idea to explore: Stock Returns indices data. You can visualize and compare different stock market returns for various indices, at different points in time. You can easily download the up to date stock market information from the finance yahoo website :

3. Box Plot Data Visualization Project Ideas

Box plot is a chart that might seem a bit intimidating or foreign if you’re seeing it for the first time. But nothing is too complicated once you get to know it better. We use the box to represent numerical data via quartiles. The whiskers that you sometimes see on top of this type of chart show the variability of the data. In such cases, we call it a box and whiskers plot.

Project-wise, we continue with the stock market theme because opening and closing prices on the stock market is one of the prime use cases of this visualization. And, of course, you can check out yahoo finance for the most current data.

4. Word Cloud Data Visualization Project Ideas

When it comes to data visualization examples, word clouds are often neglected, when in fact, they can be quite useful. Recently, they’ve found a place aiding text data analysis. Turns out, when performing sentiment analysis, word clouds can be tremendously helpful to find common topics within a cluster. Therefore, any time you’re looking at the most common items within a topic, word clouds can be a helpful way of visualizing your data.

Project idea? Any type of top 10 list, or most popular word search. Why not do a word cloud on the subject of top data visualization projects? Or head over to the Large Movie Reviews Dataset and try data visualizations based on their data.

5. Map Data Visualization Project Ideas

Being able to chart and interpret geographical data is one of the utmost skills required for a data viz expert. Depending on what software you use, this can vary in terms of difficulty. The free data visualization software most equipped to handle geographical data is probably Tableau and I recommend using it if there are no specific software requirements. Or you could also try R’s highcharter or Python plotly module (alternatively cartopy, which is based on matplotlib) if you’d prefer statistical analysis tools for visual communication.

An interactive map of Australia’s bioluminescence organisms is one of the best visualization projects just in general. Why not try and recreate the result yourself?

6. Graph Network Data Visualization Project Ideas

This type of visualization usually reflects complex systems where the importance is placed on the interaction between the elements. Despite being intricate, networks are one of the most inspiring topics in terms of dataviz, as they show that information is beautiful when translated in the correct form. Think infrastructure, social networks or biological pathways such as genetic pathways or integrated systems – all of them can be displayed with the help of a network.

If you’re looking for graph network data viz project ideas, you can head over to the network repository and explore numerous data sets on a variety of topics . The great news is that you can directly visualize each data set on the same site using their interactive tool. And maybe it’s only me but it’s great fun exploring all the different networks.

7. Race Chart Data Visualization Project Ideas

The race bar chart is an animated bar chart, showing the development of an entity (usually top 10) over time. Recently made popular by Data is Beautiful YouTube channel . There are numerous interesting races in stock, for instance, the most popular sci-fi Movies from 1968 until 2019 (that is my personal favourite). But hey, if you’re stuck for data visualization projects ideas here is our proposal.

Go over to Kaggle and see how to implement the bar chart race of the most populous Turkish provinces from 2007 until 2018 :

8. Correlogram Data Visualization Project Ideas

Data visualization examples run through various parts of the data science process. And correlograms are a part of the data exploratory phase that can reveal information on various relationships within our data. A correlogram displays n variables within our data on an (n-1)x(n-1) grid of subplots. On these subplots, you can display scatter plots, density plots, or histograms, each revealing different insights about your data.

For a correlogram data visualization project, you could try out a classic, like the Iris data set . In fact, any data where you have numerical features will do the trick. However, we recommend a data set you’d most likely be familiar with. This way, you can practice and delve into the different options presented with this form of visual.

9. Dendrogram Data Visualization Project Ideas

Continuing with data visualization examples from data science, we delve straight into machine learning with a technique used in unsupervised learning – the dendrogram. A dendrogram is a type of tree used for the hierarchical representation of points and is the main data visualization used for hierarchical clustering solutions. In fairness, results in machine learning, tend to be hard to visualize. That is one of the reasons why the field is considered hard to understand… Without any visual, it’s hard to develop an intuition of the matter. That’s why we couldn’t skip the chance to include this data visualization example.

Any type of clustering data set will do for such a project. You can visit the UCI Machine Learning Repository and check out their clustering data sets . Just a small tip. If you’re using hierarchical clustering, a large data set might require extra computing time. So keep that in mind.

10. Heatmap Data Visualization Project Ideas

Heatmap visualization is surely one of the most effective ways to intuitively show relationships between variables. What makes a heatmap stand apart is the excellent use of colors that contribute to the intuitive understanding of the plot. With a heatmap, you can observe the correlation between variables within your data and find dependencies.

The Heatmap is yet another crucial element for data analysis (or beginning stages of machine learning tasks).

So, to wrap up our list on a high note, here is an idea for a data visualization project with widespread application in data science. In fact, it is the same suggestion we started with: flight delays . There is hardly a better example of how data visualizations are interconnected.

Bonus Data Visualization Project Idea

If you’re eager for more ideas, here is another of my favorite data visualization examples, which features microbial life represented as a heatmap .

Ready to Learn Data Visualization?

Looking for data visualization training that will teach you how to turn any bad data visualization into a great one? Check out our data visualization course where you’ll learn how to create stunning data visualizations with free data visualization tools: Python, R, Tableau, and Excel.

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Elitsa is a Computational Biologist with a strong Bioinformatics background. Her courses in the 365 Data Science Program - Data Visualization, Customer Analytics, and Fashion Analytics - have helped thousands of students master the most in-demand data science tools and enhance their practical skillset. In her spare time, apart from writing expert publications, Elitsa loves hiking and windsurfing.

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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

• Which are the best tools and software for big data processing?
• Evaluate the security issues that face big data.
• An analysis of large-scale data for social networks globally.
• The influence of big data storage systems.
• The best platforms for big data computing.
• The relation between business intelligence and big data analytics.
• The importance of semantics and visualization of big data.
• Analysis of big data technologies for businesses.
• The common methods used for machine learning in big data.
• The difference between self-turning and symmetrical spectral clustering.
• The importance of information-based clustering.
• Evaluate the hierarchical clustering and density-based clustering application.
• How is data mining used to analyze transaction data?
• The major importance of dependency modeling.
• The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

• Discuss the privacy issues in big data.
• Evaluate the storage systems of scalable in big data.
• The best big data processing software and tools.
• Data mining tools and techniques are popularly used.
• Evaluate the scalable architectures for parallel data processing.
• The major natural language processing methods.
• Which are the best big data tools and deployment platforms?
• The best algorithms for data visualization.
• Analyze the anomaly detection in cloud servers
• The scrutiny normally done for the recruitment of big data job profiles.
• The malicious user detection in big data collection.
• Learning long-term dependencies via the Fourier recurrent units.
• Nomadic computing for big data analytics.
• The elementary estimators for graphical models.
• The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

• Evaluate the data mining process.
• The influence of the various dimension reduction methods and techniques.
• The best data classification methods.
• The simple linear regression modeling methods.
• Evaluate the logistic regression modeling.
• What are the commonly used theorems?
• The influence of cluster analysis methods in big data.
• The importance of smoothing methods analysis in big data.
• How is fraud detection done through AI?
• Analyze the use of GIS and spatial data.
• How important is artificial intelligence in the modern world?
• What is agile data science?
• Analyze the behavioral analytics process.
• Semantic analytics distribution.
• How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

• The difference between big data analytics and traditional data analytics methods.
• Why do you think the organization should think beyond the Hadoop hype?
• Does the size of the data matter more than how recent the data is?
• Is it true that bigger data are not always better?
• The debate of privacy and personalization in maintaining ethics in big data.
• The relation between data science and privacy.
• Do you think data science is a rebranding of statistics?
• Who delivers better results between data scientists and domain experts?
• According to your view, is data science dead?
• Do you think analytics teams need to be centralized or decentralized?
• The best methods to resource an analytics team.
• The best business case for investing in analytics.
• The societal implications of the use of predictive analytics within Education.
• Is there a need for greater control to prevent experimentation on social media users without their consent?
• How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

• The machine learning algorithms are used for fall recognition.
• The divergence and convergence of the internet of things.
• The reliable data movements using bandwidth provision strategies.
• How is big data analytics using artificial neural networks in cloud gaming?
• How is Twitter accounts classification done using network-based features?
• How is online anomaly detection done in the cloud collaborative environment?
• Evaluate the public transportation insights provided by big data.
• Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
• Discuss the current data lossless compression in the smart grid.
• How does online advertising traffic prediction helps in boosting businesses?
• How is the hyperspectral classification done using the multiple kernel learning paradigm?
• The analysis of large data sets downloaded from websites.
• How does social media data help advertising companies globally?
• Which are the systems recognizing and enforcing ownership of data records?
• The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

• The various errors and uncertainty in making data decisions.
• The application of big data on tourism.
• The automation innovation with big data or related technology
• The business models of big data ecosystems.
• Privacy awareness in the era of big data and machine learning.
• The data privacy for big automotive data.
• How is traffic managed in defined data center networks?
• Big data analytics for fault detection.
• The need for machine learning with big data.
• The innovative big data processing used in health care institutions.
• The money normalization and extraction from texts.
• How is text categorization done in AI?
• The opportunistic development of data-driven interactive applications.
• The use of data science and big data towards personalized medicine.
• The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

• The data-centric network of things.
• Big data management using artificial intelligence supply chain.
• The big data analytics for maintenance.
• The high confidence network predictions for big biological data.
• The performance optimization techniques and tools for data-intensive computation platforms.
• The predictive modeling in the legal context.
• Analysis of large data sets in life sciences.
• How to understand the mobility and transport modal disparities sing emerging data sources?
• How do you think data analytics can support asset management decisions?
• An analysis of travel patterns for cellular network data.
• The data-driven strategic planning for citywide building retrofitting.
• How is money normalization done in data analytics?
• Major techniques used in data mining.
• The big data adaptation and analytics of cloud computing.
• The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

• How is ultra-targeted marketing done?
• The transition of A/B testing from digital to offline.
• How can big data and A/B testing be done to win an election?
• Evaluate the use of A/B testing on big data
• Evaluate A/B testing as a randomized control experiment.
• How does A/B testing work?
• The mistakes to avoid while conducting the A/B testing.
• The most ideal time to use A/B testing.
• The best way to interpret results for an A/B test.
• The major principles of A/B tests.
• Evaluate the cluster randomization in big data
• The best way to analyze A/B test results and the statistical significance.
• How is A/B testing used in boosting businesses?
• The importance of data analysis in conversion research
• The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

• Assess the benefits and barriers of big data in the public sector.
• The best approach to smart city data ecosystems.
• The big analytics used for policymaking.
• Evaluate the smart technology and emergence algorithm bureaucracy.
• Evaluate the use of citizen scoring in public services.
• An analysis of the government administrative data globally.
• The public values are found in the era of big data.
• Public engagement on local government data use.
• Data analytics use in policymaking.
• How are algorithms used in public sector decision-making?
• The democratic governance in the big data era.
• The best business model innovation to be used in sustainable organizations.
• How does the government use the collected data from various sources?
• The role of big data for smart cities.
• How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

• Who uses big data analytics?
• Evaluate structure machine learning.
• Explain the whole deep learning process.
• Which are the best ways to manage platforms for enterprise analytics?
• Which are the new technologies used in data management?
• What is the importance of data retention?
• The best way to work with images is when doing research.
• The best way to promote research outreach is through data management.
• The best way to source and manage external data.
• Does machine learning improve the quality of data?
• Describe the security technologies that can be used in data protection.
• Evaluate token-based authentication and its importance.
• How can poor data security lead to the loss of information?
• How to determine secure data.
• What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

• Evaluate the 5G networks and IoT.
• Analyze the use of Artificial intelligence in the modern world.
• How do ultra-power IoT technologies work?
• Evaluate the adaptive systems and models at runtime.
• How have smart cities and smart environments improved the living space?
• The importance of the IoT-based supply chains.
• How does smart agriculture influence water management?
• The internet applications naming and identifiers.
• How does the smart grid influence energy management?
• Which are the best design principles for IoT application development?
• The best human-device interactions for the Internet of Things.
• The relation between urban dynamics and crowdsourcing services.
• The best wireless sensor network for IoT security.
• The best intrusion detection in IoT.
• The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

• The best cloud computing platforms for big data analytics.
• The parallel programming techniques for big data processing.
• The importance of big data models and algorithms in research.
• Evaluate the role of big data analytics for smart healthcare.
• How is big data analytics used in business intelligence?
• The best machine learning methods for big data.
• Evaluate the Hadoop programming in big data analytics.
• What is privacy-preserving to big data analytics?
• The best tools for massive big data processing
• IoT deployment in Governments and Internet service providers.
• How will IoT be used for future internet architectures?
• How does big data close the gap between research and implementation?
• What are the cross-layer attacks in IoT?
• The influence of big data and smart city planning in society.
• Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

• Which are the most used languages in big data?
• How is scala used in big data research?
• Is scala better than Java in big data?
• How is scala a concise programming language?
• How does the scala language stream process in real-time?
• Which are the various libraries for data science and data analysis?
• How does scala allow imperative programming in data collection?
• Evaluate how scala includes a useful REPL for interaction.
• Evaluate scala’s IDE support.
• The data catalog reference model.
• Evaluate the basics of data management and its influence on research.
• Discuss the behavioral analytics process.
• What can you term as the experience economy?
• The difference between agile data science and scala language.
• Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

• The biggest investment is in big data analysis.
• How are multi-cloud and hybrid settings deep roots?
• Why do you think machine learning will be in focus for a long while?
• Discuss in-memory computing.
• What is the difference between edge computing and in-memory computing?
• The relation between the Internet of things and big data.
• How will digital transformation make the world a better place?
• How does data analysis help in social network optimization?
• How will complex big data be essential for future enterprises?
• Compare the various big data frameworks.
• The best way to gather and monitor traffic information using the CCTV images
• Evaluate the hierarchical structure of groups and clusters in the decision tree.
• Which are the 3D mapping techniques for live streaming data.
• How does machine learning help to improve data analysis?
• Evaluate DataStream management in task allocation.
• How is big data provisioned through edge computing?
• The model-based clustering of texts.
• The best ways to manage big data.
• The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

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Top 10 GraphQL Projects Ideas for Beginners

If you’re a beginner looking to dive into the world of GraphQL, you’re in the right place. GraphQL, a powerful query language for APIs, is gaining popularity for its flexibility and efficiency in data retrieval. Whether you’re just starting out or looking to sharpen your skills, working on projects is a fantastic way to learn.

Building new projects can be the best way to develop the skills and master those skills. We all know the importance of projects as they help freshers and a complete beginner to get some hands-on experience in their respective fields. GraphQL was open-sourced in 2015 and since then it has been popular among software developers who are looking for a way to develop kinds of stuff with and for GraphQL.

In this article, we’ll explore the top 10 GraphQL project ideas tailored for beginners. These projects will not only help you grasp the fundamentals of GraphQL but also give you practical experience to showcase in your portfolio. Let’s get started on your GraphQL journey!

What is GraphQL?

GraphQL is a type of query language and server-side run-time that is used for application programming interfaces which provide API clients with exactly the data which is been requested. Software developers can develop real-time data through GraphQL’s open-source capabilities for data monitoring, and data mutation. In order to develop the APIs using GraphQL a server must host the API and the customer that connects to an application endpoint.

There are various GraphQL Projects which helps the developers to get some practical experience about GraphQL. Some of the top 10 GraphQL Project ideas are mentioned below:

1. Recipe Application

A recipe application is a type of digital platform that offers users a wide range of collection of culinary instructions for preparing multiple dishes. This project consist of various features like recipe search, user profiles, meal planning, cooking timers, and step-by-step instructions. This recipe application helps the users plan their meals for the week based on their requirements.

Key Features:

• With the help of GraphQL, developers can develop a GraphQL API for a recipe applications where the users can query recipe, view categories of foods, ingredients and many more.
• They also allow the users to add new recipes and retrieve a list of the popular recipes.
• These types of project apps provide a convenient and enjoyable way to explore new recipes and improve cooking skills.

2. To-do list manager

To-do list manager is one of the beginners friendly project idea in GraphQL. To-do List is a simple task management application which helps you to get stuff done. With the help of GraphQL you can develop a GraphQL API which is used to manage the to-do lists. This project idea mainly helps the beginners to write down the priority tasks.

• The users should be able to develop, modify, and delete with the options to filter by priority.
• It is a project that helps the users to jot down tasks and make sure you don’t forget which tasks needs to be done.
• This project is a ultimate beginner friendly project which builds the basic foundation of GraphQL and every beginner should develop this projects to get their clarity in GrapQL.

3. Social Media Profile Viewer

Social media profile viewer operates through a blend of web technologies and API interactions. It uses web scraping and API calls to extract and present profile information in a user-friendly interface. With the help of GraphQL you can develop a simple GraphQL API to fetch the users posts, profile, and comments.

• This type of project also includes features such as follow, unfollow users and liking the posts. Therefore, a good private profile viewer offers access to the user’s feed, including profile pictures and posts.
• By simulating a user’s actions, viewers can access certain data points associated with a profile.
• This project idea helps the user to get an insights about the social media platforms and how these platforms works.

4. Music Player

Music player is another beginners friendly project idea of GraphQL. These type of project apps have emerged as indispensable tools for music enthusiasts. The music player consist of different genre musics, audio features and so on. It also consist of features where users can save their best music playlists.

• These project consist of high-quality audio and user-friendly interfaces, these apps curate your music library with intuitive organization, ensuring easy access to your favourite tunes.
• With the help of GraphQL you can easily develop a GraphQL API for managing the music library and the users can query artists, tracks and develop playlists.
• These apps provide a user-friendly interface to navigate through music files stored on the device.

Must Read – How to build a simple music player app

5. Bookstore API

The Bookstore API is another project idea of GraphQL which is designed to showcase the fundamental features of Axe API. It includes three tables such as users, books, and orders with corresponding endpoints. In this projects the users can search for their required books or saved them so that they can refer it in future.

• With the help of GraphQL you can develop a GraphQL API for a bookstore where the users can query authors, books and genres.
• These type of projects includes features like searching of the required books by title, author and filtering by genre.
• These projects provides a better insight about variety of books for the bookworms.

6. Fitness Tracker

Fitness trackers are the simple applications that provide feedback on heart rate readings, encouraging users to stay active and achieve their fitness goals. These projects also consist of features where users can track their physical activities, set goals and view their detailed reports. The users can develop and manage the profiles with personal information.

• These applications helps you to know more about your health and stay healthy, you can implement GraphQL API for a fitness tracker application.
• Users can also use these applications to log workouts, query workout history, track progress and statistics.
• In this project the backend is powered by GraphQL which provides an effective and flexible way to query and manipulate the fitness data.

Also Read – How to Design a Database for Health and Fitness Tracking

7. Event Planner Application

An event planner project application is a type of software application which is used to design and help the users to organize and manage the events more effectively. Hence these type of applications are beneficial for arranging personal event like weddings and parties like conferences and meetings.

• These projects are used for multiple types of events such as in meetings, parties, wedding and conferences.
• With the help of event planner app you can develop a GraphQL API for an event planning application.
• These applications also allows the users to develop events, RSVP and view the upcoming events by date or categories.

8. Library Management System

A Library Management System is a software application used by libraries to manage their resources efficiently. With the help of GraphQL you can create a GraphQL API for managing the library’s collection. These type of projects also allows the users to query the available books, manage their accounts and check out books.

• While developing a library management system using GraphQL which involves designing a backend which uses GraphQL API for querying and managing data about members and transaction.
• It helps in handling the incoming requests from the customers and executes the queries to interact with the database.
• It is also used to manage and automate different library operations such as circulation, cataloging and so on.

9. Movie Database Application

A movie database is a type of application which is designed to manage the information about the movies. With the help of GraphQL you can develop a GraphQL API for a movie database where the users can search for their favourite movies, actors and the directors by including the filtering by genre, ratings or release years.

• GraphQL mainly involves setting up a backend which provides GraphQL for interacting with the movie data.
• These type of projects application can be website based, desktop or mobile and can be used for personal and business purposes.
• Users are able to search and select their favourite film depending on their requirements.

10. E-commerce Product Catalog

Ecommerce product catalog management is the process of storing and organizing all of an ecommerce business’s product data. It involves entering information like product pricing, descriptions, and specifications into a digital catalog management system. This project helps the users to sort products by price, popularity and so on.

• With the help of GraphQL users can develop a GraphQL API for an ecommerce platform for query products, reviews and categories.
• These project also consist of multiple features such as product search, filtering and adding to cart.
• These type of project provides a detailed information about the products including the specifications, images and price.

Must Read – E-commerce Product Catalog

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These were the top 10 of GraphQL projects which help the beginners to get some real-world knowledge about the real-world applications used in GraphQL. These projects can also be used to showcase their expertise in front of the interviewer and to stand out among multiple developers. Everybody knows the importance of projects in qualifying an interview and getting some hands-on experience as a fresher. Hence in this article provides a piece of detailed knowledge has been provided about the GraphQL projects.

What do you mean by GraphQL?

GraphQL is a type of query language which is used for application programming interfaces that prioritizes providing customer the data which they request. It also allows the developers to make the requests to fetch data from different data sources with single API call.

What is the use of GraphQL?

GraphQL is used to make the APIs fats, developer-friendly and flexible. It can also be used to deployed within the integrated development environment which is known as GraphiQL. It also provides flexibility to add or discard fields without affecting the existing queries.

What are the advantages of GraphQL?

There are various advantages of using GraphQL and developing project related to it. Some of the advantages are mentioned below: GraphQL calls are handled in a single round trip and the clients gets what they requests with no overfetching. It also allows an application API to evolve without even breaking the existing queries. Multiple open source GraphQL extensions are also available to offer features and GraphQL is also introspective.

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Economics JIW - Tips for Choosing a Topic: Home

Choosing a topic.

Choosing a topic that can answer an economic research question is challenging.  Some tips:

•  Ripped from the headlines rarely makes a good economic paper.  You will be using data to determine causation or correlation.  Sometimes a similar event can be used.  Topics such as artificial intelligence may make a good policy paper but not a good economic one due to lack of data.
• Literature Review: Your JIW should use primarily scholarly sources.  Start with Econlit (the database of the American Economic Association).  Econlit indexes major journals, working papers, conference proceedings, dissertations, and chapters in critical books. It takes a long time for scholarly literature to appear.   Preprints are called working papers in economics and major ones are indexed in Econlit.  Y ou are your own research team and have limited time.  Many articles are written over a couple of years and involve many people gathering and cleaning the data. Some starting places: see https://libguides.princeton.edu/econliterature/gettingstarted
• Outside of finance and some macroeconomic data, most data will not have many points in time.  Data determines the methods used .   While a linear regression can be great for time series data, it is likely not what you will use for survey data.
• Longitudinal or panel study :  same group of individuals is interviewed at intervals over a period of time.  This can be very useful to observe changes over time. Keep in mind when using a long running longitudinal dataset that the panel generally is not adding new participants so may not reflect today’s demographics.
• Cross-sectional study :  data from particular subjects are obtained only once.  While you are studying different individuals each time, you are looking at individuals with similar demographic characteristics.  Demography is typically rebalanced to reflect the population.
• Summary statistics : aggregated counts of survey or administrative data.
• Typically around a 2 year time lag from the time the survey data is collected to the time of release.  The Economic Census and Census of Agriculture take about 4 years for all data to be released.  Many surveys never release the microdata.
• Very little subnational data is available and is often restricted when available.   State level macro data for the United States is more prevalent.  City level data is often a case study or only available for very large cities.
• Many micro-level datasets are restricted. It is not uncommon to wait a year before getting permission or denial to use the data.  Each organization has its own rules.
• Historical data in electronic format prior to 1950 is rare. Most governmental links provide current data only.
• What is measured changes over time .  Do not assume modern concepts were tracked in the past.  Definitions of indicators often change over time.
• Data cannot be made more frequent.  Many items are collected annually or even once a decade.  Major macroeconomic indicators such as GDP tend to be quarterly but some countries may only estimate annually. 
• What exists for one country may not exist for another country. Data is generally inconsistent across borders .
• Documentation is typically in the native language .
• Always look at the methodology. The methodology section is one of the most important parts of the paper. Someone should be able to replicate your work. Describe the dataset and its population. Describe how the data was subset, any filters used, and any adjustment methods. While you are likely not trying to publish in American Economic Review  or Journal of Finance , these are the gold standards.  See how they layout the articles and in particular the methodology and data sections.
• The basic question to ask when looking for economic data is " who cares about what i am studying ?"  Unfortunately, the answer may be no one. Ideally, look for an organization that is concerned with your research as part of its mission. Examples include the International Labor Organization or the Bureau of Labor Statistics focusing on labor research; the International Monetary Fund or the Board of Governors of the Federal Reserve System focusing on monetary and fiscal concerns; the World Bank focusing on development; and the World Health Organization focusing on health. This does not mean these organizations collect data on all topics related to that field.
• Find a topic for which there is literature and data but allows room to add a contribution.  Topics such as sports and music are popular due to personal interests but may not make good research topics due to lack of data and overuse.

   More tips:

• Data is typically not adjusted for inflation.  It is usually presented in current (nominal) currency.  This means the numbers as they originally appeared.  When data has been adjusted for inflation (constant or real), a base year such as 2020 or 1990 will be shown.  If a base year is not provided, then data is current and therefore not adjusted for inflation.  If given a choice, choose current dollars.  Data is often derived from different datasets and many will use different base years.  Adjust everything at the end.  It is easier than doing reverse math!
• While most datasets are consistent within the dataset for currency used such as all in US Dollars or Euro or Japanese Yen or each item in local currency, some will mix and match.  LCU is a common abbreviation meaning local currency units. Consider looking at percent changes rather than actual values.  If adjusting use the exchange rate for each period of time, not the latest one.
• Economic indicators may be either seasonally adjusted or not seasonally adjusted.  This is very common for employment and retail sales.   Unless something says it is seasonally adjusted, it is not.  Be consistent and note in methodology.

Librarians are here to help!  Librarians can help to devise a feasible topic, assist with the literature search, and choose appropriate data.  Your data may fall into multiple categories.  Think of the primary aspect of your topic in terms of first contact.  Do not email librarians individually.  If unsure who to contact either put all that apply on same email or email just one.  If that person is not the best, they will refer you.  

Bobray Bordelon Economics, Finance, & Data Librarian   [email protected]

Charissa Jefferson

Labor Librarian [email protected]

Mary Carter Finance and Operations Research Librarian [email protected]

Data workshops

• Environmental and energy data  (Bordelon), 9/23/2024  - 7:30-8:50 pm
• Health, Crime and other Socioeconomic Data  (Bordelon), 9/23/2024 and 10/02/2024 - 3-4:20 pm 
• Macroeconomics and trade data  (Bordelon), 9/25/2024 and 9/30/2024 - 3-4:20 pm
• Finance data  (Carter), 9/23/2024 and 9/25/2024 - 3-4:20 pm
• Labor and education data  (Jefferson), 9/23/2024 and 9/25/2024 - 3-4:20 pm

Workshops listed twice have the same content and are done as an opportunity to fit your schedule.  While you must attend at least one data workshop, it is wise to attend more than one.  If in a certificate program, with the exception of political economy which has to be incorporated into your JIW, other programs have different requirements which are typically for your senior year.  As an example, if in finance, if you choose not to explore a finance topic this year you will still need to incorporate in your senior theses so try and attend a finance workshop in addition to your topical workshop for your JIW since these are intended to help you for your time at Princeton and both the JIW but also the senior thesis.

• Last Updated: Aug 28, 2024 9:32 AM
• URL: https://libguides.princeton.edu/ECOJIWTopics

The End of the Zeitenwende

Evaluating the results of the ‘Zeitenwende’, Germany’s supposed security transformation, shows that it has failed. A two-year DGAP project concludes that the Zeitenwende, proclaimed by Chancellor Olaf Scholz, no longer carries political force and should be abandoned as a term of use. By presenting strategic continuity as change, it has left Germany unprepared to face major (geo)political and (geo)economic challenges, and has eroded influence with allies. Germany now needs a comprehensive strategic reset - and bold leadership in domestic as well as foreign policy - to arrest its decline and ensure its security, prosperity, and democracy.

Germany’s Zeitenwende has failed. That is the sweeping, yet inescapable, conclusion after a two-year evaluation by the German Council on Foreign Relations’ (DGAP)  Action Group Zeitenwende (AGZ) . The project brought together German and international politicians, officials, experts and business representatives in different formats, building a network as well as knowledge. In more than forty events and over thirty publications we analyzed and explored Germany’s geostrategic positioning and choices and presented options to help the country master its geopolitical and geoeconomic challenges in the wake of Russia’s full-scale invasion of Ukraine on February 24, 2022. 

German Chancellor Olaf Scholz proclaimed, in a  landmark speech just three days later, that Russia’s attack marked a “Zeitenwende”: a historic turning point. He demanded that, after years of neglect of its defense and recklessness or naivete (depending on your view) in its  geopolitical positioning , Germany must now rise to the challenge of the changing times. Scholz outlined what grew into the five key elements of Berlin’s response to Russia’s invasion and Germany’s geostrategic repositioning, which also became known as the Zeitenwende: 

• Supporting Ukraine in its fight for freedom and democracy;
• Reducing dependency on Russian energy while continuing to pursue climate goals; 
• Taking a tougher approach to Russia and addressing threats from authoritarian states;
• Enhancing Germany’s role in strengthening the European Union (EU) and NATO;
• Arming Germany to be able to defend itself.

Based on discussions within the AGZ, the project team developed a  framework for assessing these changes, evaluating whether their speed, level of ambition, durability and coordination with international allies was sufficient to meet the geopolitical challenges that prompted them. This underpinned a broad, integrated way of understanding the Zeitenwende and Germany’s geostrategic positioning and possibilities, beyond the narrow focus on defense policy that dominated discussion after Scholz’s speech. 

Analyses by AGZ participants showed, in line with an  interim assessment , that Germany’s change has not been completed on its own terms and is dangerously inadequate to meet both the challenges that triggered it and the wider tests that have subsequently emerged. Moreover, it has  lost political and public  traction . Continuing to use the term Zeitenwende is counterproductive as it pretends that real change is ongoing when, in fact, it is more urgently needed than ever. 

The following analysis shows how and why the Zeitenwende has failed. It focuses on the role of the German coalition government’s policy and practice. It particularly examines the role of the Chancellor, Olaf Scholz, who explicitly made foreign and security policy  Chefsache” – a matter for the boss, and thus for him – and it is primarily the Federal Chancellery that has made the Zeitenwende what it is, and isn’t. 

Ukraine: Failure to Commit to Victory Undermines Substantial Support 

Germany’s support for Ukraine has been central to assessing the Zeitenwende but, because it has failed to understand that this is a war that needs to be won and then acting accordingly, Chancellor Scholz’s government has fallen damagingly short in this key area. The government has repeatedly emphasized its progress - from sending the now infamous 5000 helmets to delivering powerful offensive weapons, ( falsely ) claiming this overcame a national taboo. Scholz even felt confident enough to demand that others step up and do more, while  repeatedly   complaining that Germany’s contribution to Ukraine was underappreciated. Ostensibly, the chancellor had a point. Germany has been the second largest donor after the US and, by June 2024, had spent  EUR 33 billion  on aid (civilian, humanitarian, financial, military) and nearly EUR  26 billion on hosting Ukrainian refugees – the highest level of support to Ukraine in absolute financial terms of any European country.

Yet, the overall figures don’t tell the full story, and frustrated allies have  good grounds for criticizing Berlin. Germany has by far Europe’s largest economy and, if it were truly committed to Ukraine’s fight, would be expected to contribute by far the largest amount. Relative to GDP, however,  Germany falls below the proportion allocated by twelve other European states. Many of these  countries share the  German foreign office’s position that “Ukraine’s security is our security,” but act on it more convincingly. It is also true that other European states, notably Italy and Spain but also France and the UK, should do more. Yet, as experts within the AGZ have argued, not doing what is in your country’s national and security interest, because others are not doing it,  is poor strategy – a problem that has plagued Berlin’s approach.

In war, speed matters, and, to the despair of Ukrainians and the  anger of allies, Germany has  dragged its feet in  delivering howitzers, infantry fighting vehicles (IVFs), rocket artillery and main battle tanks (MBTs) – while blocking other countries from sending tanks it had sold them. The cost of this  delay was measured in Ukrainian lives and  squandered strategic advantage , allowing Russia to dig in and regroup, lengthening both the war and  Kyiv’s path to victory . 

What Germany has (and hasn’t) provided also matters. Scholz’s refusal, in 2023 and 2024, to send Taurus cruise missiles hindered Ukraine’s strategic position. Spurning  demands from his coalition partners, the opposition Christian Democratic and Christian Social Union parties and international allies, Scholz instead made  unconvincing   excuses . Despite delays, Germany has led on sending air defense assets, which help Ukraine survive. Yet, as AGZ members concurred, targeting the arrow, not the archer, does not stop Russia launching missiles in the first place – and does not help win the war.

And that is the key problem.  Chancellor Scholz has never said that Ukraine should win – and his government’s policy reflects that. For AGZ members, this epitomized the failure of the Zeitenwende, and the wider and deeper strategic shortcomings that persist despite the promise of change. Berlin is now  cutting future funding to Ukraine,  ostensibly for budgetary reasons , but even the large absolute sums it has committed so far have not been aimed at securing victory. For allies who back Ukraine to win because they see it as essential to European security and the creation of a viable and robust future European security order, this has undermined what should have been a common goal. Like many AGZ members, they thus see Berlin’s approach as having been,  in effect , an expensive way to make Europe  less safe . 

Energy Policy: Rapid Change Marred by Questionable Direction and Durability 

Diversification away from Russian gas has been the biggest success of the Zeitenwende. Moscow stopped supplying Germany with gas in late 2022, capping a withdrawal process from the German side that proceeded faster than many in Berlin had thought possible. The rapid completion of two large liquified natural gas (LNG) terminals on the Baltic Sea coast to allow for alternative energy suppliers was hailed by Chancellor Scholz (and many AGZ members) as evidence of a “‘new German speed.” 

AGZ members were less convinced by the government’s efforts to find a geopolitically, ecologically, and economically viable energy mix. The desire to both quickly get off Russian gas and to complete Germany’s nuclear power phaseout prompted Vice-Chancellor Robert Habeck (Green Party) to increase the use of  high-polluting coal . Habeck and Scholz struck LNG supply deals with Norway, Qatar and Azerbaijan, with the latter two creating new dependencies on authoritarian regimes. 

Berlin’s long-term plan hinges on renewables. While in 2023, Germany generated half its power needs from renewable energy sources, it has  less than half the wind and solar capacity it needs to hit its target of an 80 percent renewables share by 2030. Furthermore, Germany’s fluctuating wind and solar power means additional, stable sources are needed as back up. Without a  nuclear option, the sources of Germany’s gas will remain  geopolitically pertinent . Its energy dependencies on authoritarian regimes were seen as problematic by many AGZ members.

AGZ members were also concerned that Germany would remain dependent on Chinese materials and components for its wind and solar installations. In the event of a conflict in the Taiwan Strait, there would be extreme pressure on allies, including Germany,  from the United States to cut business with China, which would derail Berlin’s green transition (and much else). AGZ experts further highlighted that fragmented capital markets and consequent lack of scaling-up of domestic and European green innovation also means that Germans, like other Europeans pay for rather than profit from their green transition.

Despite its impressive speed, the change in German energy policy may portend trouble, including with allies who feel un-consulted on Berlin’s approach, including its  unilateral EUR 200 billion intervention to keep domestic prices down. Like the rapid construction of the LNG terminals, the size of that intervention – more than total support for Ukraine and the Bundeswehr special fund combined – shows that political will and leadership are the main determinants of change, or lack thereof.

Approach to Authoritarian States: Shaky in Practice, Misguided in Strategy

From the start, the Zeitenwende’s claim of radical change in Germany’s approach to authoritarian states was in doubt. Despite the imperative to abandon the country’s  disastrous   Russia policy – which allies in Central and Eastern Europe (CEE) and AGZ members agreed had  contributed to the full-scale war in Ukraine – Chancellor Scholz declared in the Zeitenwende speech that: “in the long term, security in Europe cannot be achieved in opposition to Russia.”

While  condemning Russia’s barbaric prosecution of its war in Ukraine, Chancellery officials also emphasized the importance of  future relations with Russia , and used Moscow’s perceptions as  spurious excuses   to not send tanks to Ukraine. Despite his Social Democratic) Party’s (SPD’s)  deep ties to Russia, Scholz insisted there would be no return to  business as usual , but nonetheless expressed his desire to “come back” to “ the peace order that worked ” with Russia. AGZ members argued that this order had mainly worked to Germany’s short-term economic benefit and to enrich, entrench and enable the authoritarian regime in Moscow. 

AGZ members also strongly criticized the Chancellery’s repeated signaling of  fear s that Russia might use  nuclear weapons , noting that it displayed a failure to understand both how NATO deterrence works and the need to signal resolve. Echoing  veiled   criticisms from allied leaders, the experts in the AGZ emphasized that, while risk averse in intent, this  actually increased risk by projecting  weakness and making Germany a target for authoritarian coercion. 

Calls from  senior SPD politicians to find ways to “ freeze the war and end it later,” without Kyiv’s victory, prompted charges of  appeasing and rewarding Russian aggression. The government’s opposition to  seizing frozen Russian assets and Scholz’s adoption of a  peace chancellor mantle strengthened the impression that Germany was  too weak to deter authoritarian bullying, and thus undermined European security. 

On China (the other major authoritarian threat), the much-anticipated  government strategy (2023) failed to deliver, and Germany’s economic dependence has continued. While  acknowledging the danger of such dependency, the strategy in practice left  de-risking up to individual businesses,  several of  which instead seized the opportunity increase  their exposure to China. This has led to  rapid growth in German companies’ investment in China , despite the geopolitical risk that this imposes on German society. 

Instead of stepping in to mitigate this risk, the Chancellery has sought to  avoid an impression of China-bashing and intervened to  water down collective EU action to place tariffs on Chinese Electric Vehicles (EVs) in response to unfair Chinese practices. Experts within the AGZ further pointed to: the long-delayed but weak proposal to  phase out Huawei equipment in Germany’s 5G network; and the controversial decision to allow a Chinese company’s purchase of a significant  stake in the port of Hamburg . Overall, both AGZ members and allied policymakers have been concerned that Berlin is repeating mistakes it made with Moscow in its relations with  Beijing . Experts within AGZ argued that this also affected Berlin’s approach to Taiwan which  needs urgent attention to help head off danger both to a fellow democracy and Germany’s own interests. 

Germany’s approach to authoritarian states may superficially seem characterized by strategic incoherence. The  National Security Strategy (2023) highlighted both growing systemic rivalry between democracies and autocracies (emphasized by the Greens) and a multipolar vision of poles, formed not around values but around geography, that trade freely with each other (emphasized by the Chancellery). In practice, though, it is Scholz’s multipolarity that has driven German policy and prevented a properly robust approach to the authoritarian threats. 

This multipolar approach seeks to ignore rather than deal with Germany’s contradictions by charting an  impossible middle course between the  US and China . It is in effect a futile effort to preserve as much as possible of the pre-2022 world, from which Germany seemed to prosper even if, in reality, Berlin was storing up trouble for the future. Continuity through multipolarity goes against the meaning Scholz ascribed to the Zeitenwende in his speech: that “the world afterwards will no longer be the same as the world before.” 

EU and NATO: Insufficient Change Leaves Germany Adrift of European Allies

Poor strategy has accentuated the  contradictions of Berlin’s geopolitical positioning. Germany has  kicked its energy dependency on Russia but is reluctant to truly confront Moscow and remains reliant on the US for its security. The latter is true for many European allies, but few see themselves as being so economically beholden to China as Germany does. Amid  sharpening geopolitical competition, Berlin’s lack of a credible vision shared with European allies has hobbled Germany’s influence in its key international institutions – the EU and NATO – and diminished the effectiveness of its cooperation with key partners. 

Under the current government, Berlin’s relationship with Paris has been  consistently dysfunctional and, even when aligned, has caused trouble with others, such as when Olaf Scholz and Emmanuel Macron recently sought sweeping last-minute changes to the EU’s Strategic Agenda without consulting other leaders. While some in Berlin tried, with mixed success, to brush off criticism from Poland’s previous government as unfair  Germany-bashing , such accusations are not credible now that Germanophile Donald Tusk has taken over the helm in Poland. It was thus striking that Tusk made public his recent closed-door  criticism of Scholz’s approach to both European and German defense – and then further  publicly upbraid ed the Chancellor at a bilateral meeting.

Olaf Scholz promised to be a  bridge builder in the EU but, together with Free Democratic Party (FDP) finance minister Christian Lindner, has instead shown an  appetite for obstruction and  ineffective or  uncoordinated action. Between its Germany-first-style energy subsidy and the controversy over tariffs on Chinese EVs, Berlin tried to block the already agreed EU combustion engine phase-out, putting domestic  coalition politics and the country’s  car industry ahead of constructive European cooperation and environmental goals.

Yet, the most strategically egregious example is Scholz and Lindner’s flat  refusal to consider joint debt to fund increased defense spending, which prompted Tusk’s ire. Many European allies see a pressing need to build a capabilities-based  European pillar of NATO – to keep the US  engaged and to mitigate the effects of either a gradual or a sudden decline in Washington’s commitment. They see common EU funding, procurement and defense industry cooperation as central to achieving this aim. While it was bad enough that Germany prioritized its  debt brake over  properly investing in its own defense capabilities, its attempt to generalize this problem to the European level was seen as beyond the pale, which is why it drew stinging criticism from Tusk and others. 

Nonetheless, Chancellor Scholz  claims that his government is in lockstep with the US on Ukraine and Russia, thus making Germany a good ally. AGZ members disagreed, as while the two countries’ positions may be aligned on Ukraine and Russia, they diverge significantly on China, which bodes ill for the future given the intensifying hostility between Washington and Beijing. They also argued that, as Europeans are more directly exposed and drastically more vulnerable than the US to both the outcome of the war and future Russian aggression, we need a tougher approach than Washington’s to avoid creating a destabilizing geopolitical gray zone. Yet,  Germany has sided with the US in not committing to Ukraine’s victory and in jointly  blocking Kyiv’s NATO ambitions at the 2023 Vilnius summit. This set Germany apart from the position of many European allies, including France, the UK, Poland, the Baltic states and others, whom Scholz has since further irritated by  speaking (unrepresentatively ) for them, undermining useful “ strategic ambiguity ,” and referring to some who take a tougher line as reckless or even “ foaming at the mouth .”

Experts within the AGZ flagged two important positive exceptions: the agreement to permanently deploy a full  combat brigade to Lithuania to bolster NATO’s defense of its Eastern flank, which was unambiguously welcomed; and Berlin’s  European Sky Shield Initiative (ESSI), a promising step toward improving European air defense to which twenty countries have signed up. Unfortunately, both come with caveats. The  brigade lacks its tanks , which will take time to be delivered and may eventually be stationed in Brandenburg rather than Lithuania, and the Bundeswehr currently lacks the necessary logistics, enablers,  military mobility and funds to make the brigade effective, alongside its other commitments. Like Poland, France will not join ESSI, and it has expressed legitimate and still unresolved concerns over how ESSI’s  ballistic missile defense elements will affect Europe’s nuclear balance and Russia’s calculations. 

Overall, Germany is not living up to Chancellor Scholz’s promised “ special responsibility ” for Europe’s success and for  European security . Too often, Germany has  assumed a right to lead , while in fact failing to do so – and also failing to follow the lead of others who have a strategy that is appropriate to the geopolitical situation. Berlin’s approach shows a significant degree of  continuity , while the world, and Germany’s European allies, have changed. This diminishes Germany’s influence, as others move forward without it, and puts its own contributions and capabilities – or lack thereof – under the spotlight.

Re-Arming Germany: Too Little, Too Slow, Too Uncertain 

The centerpiece of the Zeitenwende speech was the EUR 100 billion special fund which would, supposedly, give teeth to Germany’s much-neglected armed forces, and to Scholz’s intent to build a “powerful, cutting edge, progressive Bundeswehr that can be relied upon to protect us.” Scholz  expanded this aim and undertook to become a security  guarantor for Europe by creating “the largest conventional army within the NATO framework in Europe.” “The goal,” as  Scholz confirmed , “is a Bundeswehr that we and our allies can rely on.” Moreover,  he asserted that to achieve this, and break its habit of flouting NATO defense spending guidance, “Germany will invest two percent of our gross domestic product in our defense.”

Even if the fund was mainly spent on items from an  older shopping list , AGZ experts concurred that the 35 F-35 fighter jets, 60 Chinook helicopters, 123 Leopard 2A8 MBTs, 50 Boxer armored personnel carriers, various naval assets and missiles, and upgrades for communications systems all improve the Bundeswehr’s capabilities. IRIS-T, Patriot and Arrow-3 systems and interceptors help improve Germany’s air defenses as part of ESSI. Yet, these purchases barely touch the sides of the real gaps in Germany’s defense capabilities which still need a “ quantum leap .” AGZ members agreed that the current level of procurement doesn’t match Scholz’s stated level of ambition and is  put in the shade by the scale, speed and added  combat power of  Poland’s re-armament program. 

Some experts estimate that Germany has underinvested in defense by more than EUR  600 billion , and AGZ experts argued that a  similar amount would be needed for the country to fulfil its NATO commitments. Yet, neither AGZ nor other Germany-based  experts and  industry leaders are convinced that Germany will even  sustain 2 percent spending after the  special fund runs out in 2027 , when an extra EUR 20-30 billion will need to be found annually. Like many allies, AGZ members saw this 2 percent as outdated and insufficient for NATO’s needs. Defense Minister Boris Pistorius has been clear that Germany needs to be “war ready” within five years, and that doing so in the context of failing to defeat Russia in Ukraine would require  3-3.5 percent of GDP to be spent on defense – as much as EUR 120 billion compared to  2024’s EUR 72 billion.

These figures put Germany’s latest  budget compromise (after a high-profile row) in perspective: the increase of only EUR 1.2 billion on defense was far less than the EUR 6.5 billion requested by Pistorius. This failure to seriously budget for defense, the central pillar of the Zeitenwende, fell like a hammer on the idea of meaningful German change. Despite the  multiple warnings of impending war from German military officers and experts, leaders of allied states, and even his own  defense minister ; despite the obvious shortcomings in Germany’s capabilities and its risky over-reliance on the United States; despite Pistorius’ best efforts and the demands of  politicians from  across the democratic parties; and despite allies, especially Poland and the Baltic States, showing the way, Olaf Scholz and his government have prevented rather than enabled Germany from re-arming in a way would make good on the promise of the Zeitenwende, fulfil its responsibility to allies, and equip the country to defend itself. 

Conclusion: After the Failed Zeitenwende, Germans Need Real Change

The analyses of AGZ members have shown that the Scholz government has failed to deliver meaningful change that could durably address the serious problems laid bare by Russia’s full-scale invasion of Ukraine. On support to Ukraine, defending democracy and freedom against authoritarian threats, playing a greater role in strengthening the EU and NATO and arming Germany to defend itself, the changes made have been dangerously inadequate. Even on energy policy, significant question marks remain over how the country can source energy in ecologically, economically and  geopolitically viable ways. 

The failed Zeitenwende puts Germans’ security, prosperity and freedom at risk and has diminished Berlin’s influence with key allies and partners in Europe. Many of those allies, especially in Central and Eastern Europe, the Baltic states and the Nordic countries have been through a larger change of mindset and approach. Not on Russia – as they  didn’t need to – but on the need for Europe to take care of its own security to a far greater extent than in the past. Moreover, while they are looking for coordinated, joint European ways to act together, the other big change is that they will no longer wait for Germany to come on board.  Berlin’s reckless strategy, failure to commit to team goals, and obstructive attitude mean that Germany risks isolation as its allies leave it behind. The Scholz government’s failed Zeitenwende means that  Germany is categorically no longer Europe’s “ indispensable nation .”

The good news is that, like the politicians and experts involved in AGZ, many Germans know they need real change. In AGZ focus groups, conducted in Spring 2024 by the independent research agency  D|Part (report forthcoming in September 2024), both general and engaged publics lamented Germany’s lack of leadership and government competence. This extended across both publics’ key concerns, from the war in Ukraine to Germany’s economic weakness and dependence, perceived domestic decline and waning image and influence abroad. Significantly, the engaged public saw the Zeitenwende as having been poorly defined and delivered, while the general public didn’t even know what it was. Yet, both groups knew they wanted clearer communication on the big issues they cared about, from Ukraine to defense, the economy, migration and climate change – and action to deal with them.

There is,  therefore, a political market for change. And there are different ideas about what that change should look like, including the following ideas discussed by AGZ members. 

Real Change, Strategic Shift and National Renewal 

Committing to Ukraine’s victory and Russia’s defeat (in Ukraine) was the first priority for many AGZ members, as it would have the  greatest positive, immediate strategic effect . Bringing these twin goals about requires a definition of victory, which AGZ members agreed means restoring Ukraine’s internationally recognized 1991 borders and ensuring the country is safe from future attack, which would also imply NATO and, later, EU membership. It also requires a “theory of victory” - a clear plan for how we get from the current situation to that desired victory – and the means to implement it. 

Any theory of victory should include: removing the over-cautious restrictions imposed on how and where Ukraine can use certain weapons; seizing frozen Russian state assets to help sustain Ukraine financially and fund its victory as well as recovery, including through development of its defense industrial capacity; providing more weapons and munitions (including fighter jets and long-range strike weapons, such as Taurus) from stocks or purchased, quickly, from any available sources. Germany’s contribution to Ukraine’s victory should be commensurate with its economic weight and the responsibility it should shoulder for European security.

AGZ members were clear that a genuine German re-armament is both essential and must be made complementary rather than put in competition with arming Ukraine to win, as both are essential for the country’s security, which underpins its future prosperity and freedom. They concurred that Germany’s defense investment should go much further and proceed much faster, particularly learning from Poland’s example. Placing significant orders for both Ukraine and for its own re-armament would provide a significant incentive to industry to increase both production capacity and speed. Jointly procuring the capabilities and enablers to build the  European pillar that NATO needs should also be a priority and would benefit from joint EU debt as well as defense industrial coordination, which Germany should support. 

These changes would themselves require at least two other major shifts underpinned by further, more fundamental strategic transformations in both foreign and domestic policy. First, Germany would have to become a  better team player , especially in its key institutions (the EU and NATO) by, inter alia, committing to common goals, following (not obstructing) when others lead to drive the team forward, and providing leadership in areas where it is strong but in ways that others can follow and contribute to. Becoming such a “ team power ” entails a different mode of foreign policy and diplomacy, but would play to many of the country’s strengths. Most importantly, it would require a shift in strategic worldview toward that of allies who have understood that democracies are involved in a systemic competition and must throw their weight into ensuring the free world prevails against authoritarian threats. 

Second, Germany would have to commit (much) more money to defense, focused on meeting its alliance commitments. Many experts and politicians within AGZ agreed that Germany should spend at least 3 percent of GDP annually on defense for the foreseeable future. This runs up against another need for a deeper domestic transformation: the (in)famous debt brake, which constitutionally limits the fiscal flexibility of the government, has been repeatedly used as a reason – or an excuse – for not spending more on defense, on Ukraine, or even on fixing Germany’s creaking infrastructure or accelerating its technological and green transitions. Adhering to the debt brake even when,  according to leading economists , it has become a risk for both German and European security, also reflects an ideological position that lies behind the refusal to accept common European debt for defense. 

AGZ members were divided on this issue, but that actually reflects an opportunity for constructive political debate about a crucial dimension of Germany’s future that will affect its geopolitical positioning as well as the character of its own society. Those who wish to ditch the debt brake  will have to work hard to construct the necessary two-thirds majority in the Bundestag. They will need to compellingly explain and support their spending plans and how loosening the rules would not simply lead to throwing money at problems that (also) need other solutions, as well as how they would prevent runaway debt or the kind of destabilizing economic policy that so damaged Germany (and Europe) in the past. On the other hand, those who would keep the debt brake need to explain how they will fund defense, properly contribute to European security and invest in Germany’s future prosperity – and how they will do so without exacerbating domestic social problems, which would provide fertile ground for anti-democratic forces. Several advocates of this approach also argue for cutting social spending, which needs to be considered in the context of voters’ concerns over rising prices and economic disenfranchisement, as well as income and wealth inequality. For those who would rely on economic growth, the situation is further complicated by the need to source energy in geopolitically responsible ways and rethink the country’s economic and trade models to reduce the security and alliance risks posed by dependence on China. 

Whichever route Germany’s next leaders choose, they will not be able to avoid the need for major investment, including in infrastructure, to renew the basis of the country’s competitiveness and future prosperity. Nor will they be able to escape the reality that social spending to try to maintain the status quo has failed to stem the growth of anti-democratic parties. Instead, future investment, complemented by  capital market reform to encourage, reward and scale up innovation, should focus on updating and upgrading Germany’s growth model including by accelerating the green transition, embracing technological change, and developing the skills profile and regulatory framework needed to drive prosperity. 

After the failed Zeitenwende, Germany faces thoroughgoing challenges across numerous policy fields. Scholz’s multipolarity strategy of continuity in a changed world has failed to align Germany’s security with its prosperity – and now risks both. Worse, the wasted opportunity for change, confused messaging on exposure to authoritarian threats and their potential impacts, failure to explain that necessary change implies costs which nonetheless must be borne, and fractious and ineffective government have created a risk to Germany’s democracy. To master these challenges, Germany needs a true  Grand Strategy to restore and marshal the sources of its power, and harmonize foreign,  domestic, defense, and economic policy in pursuit of clear goals.

Presenting a clear vision for a future Germany and the kind of world it wants to help shape, and then setting the strategy and committing the resources to achieve it, would create a genuine, democratic alternative to both the inadequate status quo and to the dangerous vision proposed by anti-democratic parties. It is incumbent on politicians and experts, such as those involved in the AGZ, to properly debate, in public, what that vision should be and to propose credible ways to achieve it. The task now at hand is no less than reinventing Germany’s collective identity and reinvigorating its societal purpose to ensure Germans’ future security, prosperity and freedom and their country’s place in the democratic world.

The views expressed in this policy brief are those of the author and do not necessarily reflect the views of the Action Group Zeitenwende or DGAP.

The project “Action Group Zeitenwende” cultivates the comprehensive yet coherent approach that Germany needs to better define, express, and pursue its own interests as well as the goals and values it shares with its partners. It helps build a Germany that is ready, willing, and able to act. “Action Group Zeitenwende” is funded by Stiftung Mercator.

Bibliographic data

Themen & regionen.

• Zeitenwende
• European Union
• Deutschlands erste Nationale Sicherheitsstrategie

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