data science in problem solving

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Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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5 Structured Thinking Techniques for Data Scientists

Try 1 of these 5 structured thinking techniques as you wrestle with your next data science project.

Structured thinking is a framework for solving unstructured problems — which covers just about all data science problems. Using a structured approach to solve problems not only only helps solve problems faster but also helps identify the parts of the problem that may need some extra attention. 

Think of structured thinking like the map of a city you’re visiting for the first time.Without a map, you’ll probably find it difficult to reach your destination. Even if you did eventually reach your destination, it’ll probably take you at least double the time.

What Is Structured Thinking?

Here’s where the analogy breaks down: Structured thinking is a framework and not a fixed mindset; you can modify these techniques based on the problem you’re trying to solve.  Let’s look at five structured thinking techniques to use in your next data science project .

• Six Step Problem Solving Model
• Eight Disciplines of Problem Solving
• The Drill Down Technique
• The Cynefin Framework
• The 5 Whys Technique

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1. Six Step Problem Solving Model

This technique is the simplest and easiest to use. As the name suggests, this technique uses six steps to solve a problem, which are:

Have a clear and concise problem definition.

Study the roots of the problem.

Brainstorm possible solutions to the problem.

Examine the possible solution and choose the best one.

Implement the solution effectively.

Evaluate the results.

This model follows the mindset of continuous development and improvement. So, on step six, if your results didn’t turn out the way you wanted, go back to step four and choose another solution (or to step one and try to define the problem differently).

My favorite part about this simple technique is how easy it is to alter based on the specific problem you’re attempting to solve. 

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2. Eight Disciplines of Problem Solving

The eight disciplines of problem solving offers a practical plan to solve a problem using an eight-step process. You can think of this technique as an extended, more-detailed version of the six step problem-solving model.

Each of the eight disciplines in this process should move you a step closer to finding the optimal solution to your problem. So, after you’ve got the prerequisites of your problem, you can follow  disciplines D1-D8.

D1 : Put together your team. Having a team with the skills to solve the project can make moving forward much easier.

D2 : Define the problem. Describe the problem using quantifiable terms: the who, what, where, when, why and how.

D3 : Develop a working plan.

D4 : Determine and identify root causes. Identify the root causes of the problem using cause and effect diagrams to map causes against their effects.

D5 : Choose and verify permanent corrections. Based on the root causes, assess the work plan you developed earlier and edit as needed.

D6 : Implement the corrected action plan.

D7 : Assess your results.

D8 : Congratulate your team. After the end of a project, it’s essential to take a step back and appreciate the work you’ve all done before jumping into a new project.

3. The Drill Down Technique

The drill down technique is more suitable for large, complex problems with multiple collaborators. The whole purpose of using this technique is to break down a problem to its roots to make finding solutions that much easier. To use the drill down technique, you first need to create a table. The first column of the table will contain the outlined definition of the problem, followed by a second column containing the factors causing this problem. Finally, the third column will contain the cause of the second column's contents, and you’ll continue to drill down on each column until you reach the root of the problem.

Once you reach the root causes of the symptoms, you can begin developing solutions for the bigger problem.

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4. The Cynefin Framework

The Cynefin framework, like the rest of the techniques, works by breaking down a problem into its root causes to reach an efficient solution. We consider the Cynefin framework a higher-level approach because it requires you to place your problem into one of five contexts.

• Obvious Contexts. In this context, your options are clear, and the cause-and-effect relationships are apparent and easy to point out.
• Complicated Contexts. In this context, the problem might have several correct solutions. In this case, a clear relationship between cause and effect may exist, but it’s not equally apparent to everyone.
• Complex Contexts. If it’s impossible to find a direct answer to your problem, then you’re looking at a complex context. Complex contexts are problems that have unpredictable answers. The best approach here is to follow a trial and error approach.
• Chaotic Contexts. In this context, there is no apparent relationship between cause and effect and our main goal is to establish a correlation between the causes and effects.
• Disorder. The final context is disorder, the most difficult of the contexts to categorize. The only way to diagnose disorder is to eliminate the other contexts and gather further information.

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5. The 5 Whys Technique

Our final technique is the 5 Whys or, as I like to call it, the curious child approach. I think this is the most well-known and natural approach to problem solving.

This technique follows the simple approach of asking “why” five times — like a child would. First, you start with the main problem and ask why it occurred. Then you keep asking why until you reach the root cause of said problem. (Fair warning, you may need to ask more than five whys to find your answer.)

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Key skills for aspiring data scientists: Problem solving and the scientific method

This blog is part two of our ‘Data science skills’ series, which takes a detailed look at the skills aspiring data scientists need to ace interviews, get exciting projects, and progress in the industry. You can find the other blogs in our series under the ‘Data science career skills’ tag. 

One of the things that attracts a lot of aspiring data scientists to the field is a love of problem solving, more specifically problem solving using the scientific method. This has been around for hundreds of years, but the vast volume of data available today offers new and exciting ways to test all manner of different hypotheses – it is called data science after all. 

If you’re a PhD student, you’ll probably be fairly used to using the scientific method in an academic context, but problem solving means something slightly different in a commercial context. To succeed, you’ll need to learn how to solve problems quickly, effectively and within the constraints of your organisation’s structure, resources and time frames. 

Why is problem solving essential for data scientists? 

Problem solving is involved in nearly every aspect of a typical data science project from start to finish. Indeed, almost all data science projects can be thought of as one long problem solving exercise.

To make this clear, let’s consider the following case study; you have been asked to help optimize a company’s direct marketing, which consists of weekly catalogues. 

Defining the right question 

The first aim of most data science projects is to properly specify the question or problem you wish to tackle. This might sound trivial, but it can often be one of the most challenging parts of any project, and how successful you are at this stage can come to define how successful you are by the finish.

In an academic context, your problem is usually very clearly defined. But as a data scientist in industry it’s rare for your colleagues or your customer to know exactly which problem they’re trying to solve.  

In this example, you have been asked to “optimise a company’s direct marketing”. There are numerous translations of this problem statement into the language of data science. You could create a model which helps you contact customers who would get the biggest uplift in purchase propensity or spend from receiving direct marketing. Or you could simply work out which customers are most likely to buy and focus on contacting them. 

While most marketers and data scientists would agree that the first approach is better in theory, whether or not you can answer this question through data depends on what the company has been doing up to this point. A robust analysis of the company’s data and previous strategy is therefore required, even before deciding on which specific problem to focus on.

This example makes clear the importance of properly defining your question up front; both options here would lead you on very different trajectories and it is therefore crucial that you start off on the right one.  As a data scientist, it will be your job to help turn an often vague direction from a customer or colleague into a firm strategy.

Formulating and evaluating hypotheses

Once you’ve decided on the question that will deliver the best results for your company or your customer, the next step is to formulate hypotheses to test. These can come from many places, whether it be the data, business experts, or your own intuition.

Suppose in this example you’ve had to settle for finding customers who are most likely to buy. Clearly you’ll want to ensure that your new process is better than the company’s old one – indeed, if you’re making better data driven decisions than the company’s previous process you would expect this to be the case.

There is a challenge here though – you can’t directly test the effect of changing historical mailing decisions because these decisions have already been made. However, you can indirectly, by looking at people who were mailed, and then looking at who bought something and who didn’t. If your new process is superior to the previous one, it should be suggesting that you mail most of the people in this first category, as people missed here could indicate potential lost revenue. It should also omit most of the people in the latter category, as mailing this group is definitely wasted marketing spend. 

While these metrics don’t prove that your new process is better, they do provide some evidence that you’re making improvements over what went before.

This example is typical of applied data science projects – you often can’t test your model on historical data to the extent that you would like, so you have to use the data you have available as best you can to give us as much evidence as is possible as to the validity of your hypotheses.

Testing and drawing conclusions

The ultimate test of any data science algorithm is how it performs in the real world. Most data science projects will end by attempting to answer this question, as ultimately this is the only way that data science can truly deliver value to people.

In our example from above, this might look like comparing your algorithm against the company’s current process by doing an randomised control trial (RCT), and comparing the response rates across the two groups. Of course one would expect random variation, and being able to explain the significance (or lack thereof) of any deviations between the two groups would be essential to solving the company’s original problem.

How successfully you test and draw your final conclusions, as well as well you take into account all the limitations with the evaluation, will ultimately decide how impactful the end result of the project is. When addressing a business problem there can be massive consequences to getting the answer wrong – therefore formulating this final test in a way that is scientifically robust but also helps address the original problem statement is therefore paramount, and is a skill that any data scientist needs to possess.

How to develop your problem solving skills

There are certainly ways you can develop your applied data science problem solving skills. The best advice, as so often is true in life, is to practice. Indeed, one of the reasons that so many employers look for data scientists with PhDs is because this demonstrates that the individual in question can solve hard problems. 

Websites like kaggle can be a great starting point for learning how to tackle data science problems and winners of old competitions often have good posts about how they came to build their winning model. It’s also important to learn how to translate business problems into a clear data science problem statement. Data science problems found online have often solved this bit for you, so try and focus on those that are vague and ill-defined – whilst it might be tempting to stick to those that are more concrete, real life is seldom as accommodating.

As the best way to develop your skills is to practice them, Faculty’s Fellowship programme can be a fantastic way to improve your problem solving skills. As the fellowship gives you an opportunity to tackle a real business problem for a real customer, and take the problem through from start to finish, there are not many better ways to develop, and prove, your skills in this area.

Head to the Faculty Fellowship page to find out more. 

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Data Science Solutions: Applications and Use Cases

Data Science is a broad field with many potential applications. It’s not just about analyzing data and modeling algorithms, but it also reinvents the way businesses operate and how different departments interact. Data scientists solve complex problems every day, leveraging a variety of Data Science solutions to tackle issues like processing unstructured data, finding patterns […]

Data Science is a broad field with many potential applications. It’s not just about analyzing data and modeling algorithms, but it also reinvents the way businesses operate and how different departments interact. Data scientists solve complex problems every day, leveraging a variety of Data Science solutions to tackle issues like processing unstructured data, finding patterns in large datasets, and building recommendation engines using advanced statistical methods, artificial intelligence, and machine learning techniques. 

Data Science helps analyze and extract patterns from corporate data, so these patterns can be organized to guide corporate decisions. Data analysis using Data Science techniques helps companies to figure out which trends are the best fit for businesses during various parts of the year. 

Through data patterns, Data Science professionals can use tools and techniques to forecast future customer needs toward a specific product or service.  Data Science and businesses  can work together closely in understanding consumer preferences across a wide range of items and running better marketing campaigns. 

To enhance the scope of  predictive analytics , Data Science now employs other advanced technologies such as machine learning and deep learning to improve decision-making and create better models for predicting financial risks, customer behaviors, or market trends.

Data Science helps with making  future-proofing decisions,  supply chain predictions, understanding market trends, planning better pricing for products, consideration of automation for various data-driven tasks, and so on.

For example, in sales and marketing, Data Science is mainly used to predict markets, determine new customer segments, optimize pricing structures, and analyze the customer portfolio. Businesses frequently use sentiment analysis and behavior analytics to determine purchase and usage patterns, and to understand how people view products and services. Some businesses like Lowes, Home Depot, or Netflix use “hyper-personalization” techniques to match offers to customers accurately via their recommendation engines. 

E-commerce companies use recommendation engines, pricing algorithms, customer predictive segmentation, personalized product image searching, and artificially intelligent chat bots to offer transformational customer experience. 

In recent times,  deep learning , through its use of “artificial neural networks,” has empowered data scientists to perform unstructured data analytics, such as image recognition, object categorizing, and sound mapping.  

Data Science Solutions by Industry Applications

Now let’s take a look at how Data Science is powering the industry sectors  with its cross-disciplinary platforms and tools:

Data Science Solutions in Banking:  Banking and financial sectors are highly dependent on Data Science solutions powered with big data tools for risk analytics, risk management, KYC, and fraud mitigation. Large banks, hedge funds, stock exchanges, and other financial institutions use advanced Data Science (powered by big data, AI, ML) for trading analytics, pre-trade decision-support analytics, sentiment measurements, predictive analytics, and more. 

Data Science Solutions in Marketing:  Marketing departments often use Data Science to build recommendation systems and to analyze customer behavior. When we talk about Data Science in marketing, we are primarily concerned with what we call “retail marketing.” The retail marketing process involves analyzing customer data to inform business decisions and drive revenue. Common data used in retail marketing include customer data, product data, sales data, and competitor data. Customer transactional data is used extensively in AI-powered  data analytics systems  for increased sales and providing excellent marketing services. Chatbot analytics and sales representative response data are used together to improve sales efficiency. 

The retailer can use this data to build customer-targeted marketing campaigns, optimize prices based on demand, and decide on product assortment. The retail marketing process is rarely automated; it involves making business decisions based on the data. Data scientists working in retail marketing are primarily concerned with deriving insights from the data and applying statistical and machine learning methods to inform these decisions.

Data Science Solutions in Finance and Trading:  Finance departments use Data Science to build trading algorithms, manage risk, and improve compliance. A  data scientist  working in finance will primarily use data about the financial markets. This includes data about the companies whose stocks are traded on the market, the trading activity of the investors, and the stock prices. The financial data is unstructured and messy; it’s collected from different sources using different formats. The data scientist’s first task, therefore, is to process the data and convert it into a structured format. This is necessary for building algorithms and other models. For example, the data scientist might build a trading algorithm that exploits the market inefficiencies and generates profits for the company.

Data Science Solutions in Human Resources:  HR departments use Data Science to hire the best talent, manage employee data, and predict employee performance. The data scientist working in HR will primarily use employee data collected from different sources. This data could be structured or unstructured depending on how it’s collected. The most common source is an HR database such as Workday. The data scientist’s first task is to process the data and clean it. This is necessary for insights from the data. The data scientist might use methods like  machine learning  to predict the employee’s performance. This can be done by training the algorithm on historical employee data and the features it contains. For example, the data scientist might build a model that predicts employee performance using historical data. 

Data Science in Logistics and Warehousing:  Logistics and operations departments  use Data Science to manage supply chains and predict demand. The data scientist working in logistics and warehousing will primarily use data about customer orders, inventory, and product prices. The data scientist will use data from sensors and IoT devices deployed in the supply chain to track the product’s journey. The data scientist might use methods like machine learning to predict demand.  

Data Science Solutions in Customer Service:  Customer service departments use Data Science to answer customer queries, manage tickets, and improve the end-to-end customer experience. The data scientist working in customer service will primarily use data about customer tickets, customers, and the support team. The most common source is the ticket management system. In this case, the data scientist might use methods like machine learning to predict when the customer will stop engaging with the brand. This can be done by training the algorithm on historical customer data. For example, using historical data, the data scientist might build a model that predicts when a customer will stop engaging with the brand.

Big Data with Data Science Solutions Use Cases

While Data Science solutions can be used to get insights into behaviors and processes, big data analytics indicates the convergence of several cutting-edge technologies working together to help enterprise organizations extract better value from the data that they have.

In biomedical research and health, advanced Data Science and big data analytics techniques are used for increasing online revenue, reducing customer complaints, and enhancing customer experience through personalized services. In the hospitality and food services industries, once again big data analytics is used for studying customers’ behavior through shopping data, such as wait times at the checkout. Statistics show that 38% of companies use big data to improve organizational effectiveness. 

In the insurance sector, big data-powered predictive analytics is frequently used for analyzing large volumes of data at high speed during the underwriting stage. Insurance claims analysts now have access to algorithms that help identify fraudulent behaviors. Across all industry sectors, organizations are harnessing the predictive powers of Data Science to enhance their business forecasting capabilities. 

Big data coupled with Data Science  enables enterprise businesses  to leverage their own organization data, rather than relying on market studies or third-party tools. Data Science practitioners work closely with RPA industry professionals to identify data sources for a company, as well as to build dashboards and visuals for searching various forms of data analytics in real-time. Data Science teams can now train deep learning systems to identify contracts and invoices from a stack of documents, as well as perform different types of identification for the information.

Big data analytics has the potential to unlock great insights into data across social media channels and platforms, enabling marketing, customer support, and advertising to improve and be more aligned with corporate goals. Big data analytics make research results better, and helps organizations use research more effectively by allowing them to identify specific test cases and user settings.

Specialized Data Science Use Cases with Examples

Data Science applications can be used for any industry or area of study, but the majority of examples involve data analytics for  business use cases . In this section, some specific use cases are presented with examples to help you better understand its potential in your organization.

Data cleansing:  In Data Science, the first step is data cleansing, which involves identifying and cleaning up any incorrect or incomplete data sets. Data cleansing is critical to identify errors and inconsistencies that can skew your data analysis and lead to poor business decisions. The most important thing about data cleansing is that it’s an ongoing process. Business data is always changing, which means the data you have today might not be correct tomorrow. The best data scientists know that data cleansing isn’t done just once; it’s an ongoing process that starts with the very first data set you collect. 

Prediction and forecasting:  The next step in Data Science is data analysis, prediction, and forecasting. You can do this on an individual level or on a larger scale for your entire customer base. Prediction and forecasting helps you understand how your customers behave and what they may do next. You can use these insights to create better products, marketing campaigns, and customer support. Normally, the techniques used for prediction and forecasting include regression, time series analysis, and artificial neural networks. 

Fraud detection:  Fraud detection is a highly specialized use of Data Science that relies on many techniques to identify inconsistencies. With fraud detection, you’re trying to find any transactions that are incorrect or fraudulent. It’s an important use case because it can significantly reduce the costs of business operations. The best fraud detection systems are wide-ranging. They use many different techniques to identify inconsistencies and unusual data points that suggest fraud. Because fraud detection is such a specialized use case, it’s best to work with a Data Science professional. 

Data Science for business growth:  Every business wants to grow, and this is a natural outcome of doing business. Yet many businesses struggle to keep up with their competitors. Data Science can help you understand your potential customers and improve your services. It can also help you identify new opportunities and explore different areas you can expand into. Use Data Science to identify your target audience and their needs. Then create products and services that serve those needs better than your competitors can. You can also use Data Science to identify new markets, explore new areas for growth, and expand into new industries. 

Data Science is an interdisciplinary field that uses mathematics, engineering, statistics, machine learning, and other fields of study to analyze data and identify patterns. Data Science applications can be used for any industry or area of study, but most examples involve data analytics for  business use cases . Data Science often helps you understand your potential customers and their buying needs. 

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Framing Data Science Problems the Right Way From the Start

Data science project failure can often be attributed to poor problem definition, but early intervention can prevent it..

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The failure rate of data science initiatives — often estimated at over 80% — is way too high. We have spent years researching the reasons contributing to companies’ low success rates and have identified one underappreciated issue: Too often, teams skip right to analyzing the data before agreeing on the problem to be solved. This lack of initial understanding guarantees that many projects are doomed to fail from the very beginning.

Of course, this issue is not a new one. Albert Einstein is often quoted as having said , “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute solving it.”

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Consider how often data scientists need to “clean up the data” on data science projects, often as quickly and cheaply as possible. This may seem reasonable, but it ignores the critical “why” question: Why is there bad data in the first place? Where did it come from? Does it represent blunders, or are there legitimate data points that are just surprising? Will they occur in the future? How does the bad data impact this particular project and the business? In many cases, we find that a better problem statement is to find and eliminate the root causes of bad data .

Too often, we see examples where people either assume that they understand the problem and rush to define it, or they don’t build the consensus needed to actually solve it. We argue that a key to successful data science projects is to recognize the importance of clearly defining the problem and adhere to proven principles in so doing. This problem is not relegated to technology teams; we find that many business, political, management, and media projects, at all levels, also suffer from poor problem definition.

Toward Better Problem Definition

Data science uses the scientific method to solve often complex (or multifaceted) and unstructured problems using data and analytics. In analytics, the term fishing expedition refers to a project that was never framed correctly to begin with and involves trolling the data for unexpected correlations. This type of data fishing does not meet the spirit of effective data science but is prevalent nonetheless. Consequently, defining the problem correctly needs to be step one. We previously proposed an

About the Authors

Roger W. Hoerl ( @rogerhoerl ) teaches statistics at Union College in Schenectady, New York. Previously, he led the applied statistics lab at GE Global Research. Diego Kuonen ( @diegokuonen ) is head of Bern, Switzerland-based Statoo Consulting and a professor of data science at the Geneva School of Economics and Management at the University of Geneva. Thomas C. Redman ( @thedatadoc1 ) is president of New Jersey-based consultancy Data Quality Solutions and coauthor of The Real Work of Data Science: Turning Data Into Information, Better Decisions, and Stronger Organizations (Wiley, 2019).

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10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Table of Contents

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Aakash Tandel , Former Data Scientist

Article Categories: #Strategy , #Data & Analytics

Posted on December 3, 2018

There is a systematic approach to solving data science problems and it begins with asking the right questions. This article covers some of the many questions we ask when solving data science problems at Viget.

T h e r e i s a s y s t e m a t i c a p p r o a c h t o s o l v i n g d a t a s c i e n c e p r o b l e m s a n d i t b e g i n s w i t h a s k i n g t h e r i g h t q u e s t i o n s . T h i s a r t i c l e c o v e r s s o m e o f t h e m a n y q u e s t i o n s w e a s k w h e n s o l v i n g d a t a s c i e n c e p r o b l e m s a t V i g e t .

A challenge that I’ve been wrestling with is the lack of a widely populated framework or systematic approach to solving data science problems. In our analytics work at Viget, we use a framework inspired by Avinash Kaushik’s Digital Marketing and Measurement Model . We use this framework on almost every project we undertake at Viget. I believe data science could use a similar framework that organizes and structures the data science process.

As a start, I want to share the questions we like to ask when solving a data science problem. Even though some of the questions are not specific to the data science domain, they help us efficiently and effectively solve problems with data science.

Business Problem

What is the problem we are trying to solve?

That’s the most logical first step to solving any question, right? We have to be able to articulate exactly what the issue is. Start by writing down the problem without going into the specifics, such as how the data is structured or which algorithm we think could effectively solve the problem.

Then try explaining the problem to your niece or nephew, who is a freshman in high school. It is easier than explaining the problem to a third-grader, but you still can’t dive into statistical uncertainty or convolutional versus recurrent neural networks. The act of explaining the problem at a high school stats and computer science level makes your problem, and the solution, accessible to everyone within your or your client’s organization, from the junior data scientists to the Chief Legal Officer.

Clearly defining our business problem showcases how data science is used to solve real-world problems. This high-level thinking provides us with a foundation for solving the problem. Here are a few other business problem definitions we should think about.

• Who are the stakeholders for this project?
• Have we solved similar problems before?
• Has someone else documented solutions to similar problems?
• Can we reframe the problem in any way?

And don’t be fooled by these deceivingly simple questions. Sometimes more generalized questions can be very difficult to answer. But, we believe answering these framing question is the first, and possibly most important, step in the process, because it makes the rest of the effort actionable.  

Say we work at a video game company —  let’s call the company Rocinante. Our business is built on customers subscribing to our massive online multiplayer game. Users are billed monthly. We have data about users who have cancelled their subscription and those who have continued to renew month after month. Our management team wants us to analyze our customer data.

Well, as a company, the Rocinante wants to be able to predict whether or not customers will cancel their subscription . We want to be able to predict which customers will churn, in order to address the core reasons why customers unsubscribe. Additionally, we need a plan to target specific customers with more proactive retention strategies.

Churn is the turnover of customers, also referred to as customer death. In a contractual setting - such as when a user signs a contract to join a gym - a customer “dies” when they cancel their gym membership. In a non-contractual setting, customer death is not observed and is more difficult to model. For example, Amazon does not know when you have decided to never-again purchase Adidas. Your customer death as an Amazon or Adidas customer is implied.

Possible Solutions

What are the approaches we can use to solve this problem.

There are many instances when we shouldn’t be using machine learning to solve a problem. Remember, data science is one of many tools in the toolbox. There could be a simpler, and maybe cheaper, solution out there. Maybe we could answer a question by looking at descriptive statistics around web analytics data from Google Analytics. Maybe we could solve the problem with user interviews and hear what the users think in their own words. This question aims to see if spinning up EC2 instances on Amazon Web Services is worth it. If the answer to,  “Is there a simple solution,”  is, “No,” then we can ask, “ Can we use data science to solve this problem? ” This yes or no question brings about two follow-up questions:

• “ Is the data available to solve this problem? ” A data scientist without data is not a very helpful individual. Many of the data science techniques that are highlighted in media today — such as deep learning with artificial neural networks — requires a massive amount of data. A hundred data points is unlikely to provide enough data to train and test a model. If the answer to this question is no, then we can consider acquiring more data and pipelining that data to warehouses, where it can be accessed at a later date.
• “ Who are the team members we need in order to solve this problem? ” Your initial answer to this question will be, “The data scientist, of course!” The vast majority of the problems we face at Viget can’t or shouldn’t be solved by a lone data scientist because we are solving business problems. Our data scientists team up with UXers , designers , developers , project managers , and hardware developers to develop digital strategies and solving data science problems is one part of that strategy. Siloing your problem and siloing your data scientists isn’t helpful for anyone.

We want to predict when a customer will unsubscribe from Rocinante’s flagship game. One simple approach to solving this problem would be to take the average customer life - how long a gamer remains subscribed - and predict that all customers will churn after X amount of time. Say our data showed that on average customers churned after 72 months of subscription. Then we  could  predict a new customer would churn after 72 months of subscription. We test out this hypothesis on new data and learn that it is wildly inaccurate. The average customer lifetime for our previous data was 72 months, but our new batch of data had an average customer lifetime of 2 months. Users in the second batch of data churned much faster than those in the first batch. Our prediction of 72 months didn’t generalize well. Let’s try a more sophisticated approach using data science.

• Is the data available to solve this problem?  The dataset contains 12,043 rows of data and 49 features. We determine that this sample of data is large enough for our use-case. We don’t need to deploy Rocinante’s data engineering team for this project.
• Who are the team members we need in order to solve this problem?   Let’s talk with the Rocinante’s data engineering team to learn more about their data collection process. We could learn about biases in the data from the data collectors themselves. Let’s also chat with the customer retention and acquisitions team and hear about their tactics to reduce churn. Our job is to analyze data that will ultimately impact their work. Our project team will consist of the data scientist to lead the analysis, a project manager to keep the project team on task, and a UX designer to help facilitate research efforts we plan to conduct before and after the data analysis.

How do we know if we have successfully solved the problem?

At Viget, we aim to be data-informed, which means we aren’t blindly driven by our data, but we are still focused on quantifiable measures of success. Our data science problems are held to the same standard.  What are the ways in which this problem could be a success? What are the ways in which this problem could be a complete and utter failure?  We often have specific success metrics and Key Performance Indicators (KPIs) that help us answer these questions.

Our UX coworker has interviewed some of the other stakeholders at Rocinante and some of the gamers who play our game. Our team believes if our analysis is inconclusive, and we continue the status quo, the project would be a failure. The project would be a success if we are able to predict a churn risk score for each subscriber. A churn risk score, coupled with our monthly churn rate (the rate at which customers leave the subscription service per month), will be useful information. The customer acquisition team will have a better idea of how many new users they need to acquire in order to keep the number of customers the same, and how many new users they need in order to grow the customer base. 

Data Science-ing

What do we need to learn about the data and what analysis do we need to conduct.

At the heart of solving a data science problem are hundreds of questions. I attempted to ask these and similar questions last year in a blog post,  Data Science Workflow . Below are some of the most crucial — they’re not the only questions you could face when solving a data science problem, but are ones that our team at Viget thinks about on nearly every data problem.

• What do we need to learn about the data?
• What type of exploratory data analysis do we need to conduct?
• Where is our data coming from?
• What is the current state of our data?
• Is this a supervised or unsupervised learning problem?
• Is this a regression, classification, or clustering problem?
• What biases could our data contain?
• What type of data cleaning do we need to do?
• What type of feature engineering could be useful?
• What algorithms or types of models have been proven to solve similar problems well?
• What evaluation metric are we using for our model?
• What is our training and testing plan?
• How can we tweak the model to make it more accurate, increase the ROC/AUC, decrease log-loss, etc. ?
• Have we optimized the various parameters of the algorithm? Try grid search here.
• Is this ethical?

That last question raises the conversation about ethics in data science. Unfortunately, there is no hippocratic oath for data scientists, but that doesn’t excuse the data science industry from acting unethically. We should apply ethical considerations to our standard data science workflow. Additionally, ethics in data science as a topic deserves more than a paragraph in this article — but I wanted to highlight that we should be cognizant and practice only ethical data science.

Let’s get started with the analysis. It’s  time to answer the data science questions. Because this is an example, the answer to these data science questions are entirely hypothetical.

• We need to learn more about the time series nature of our data, as well as the format.
• We should look into average customer lifetime durations and summary statistics around some of the features we believe could be important.
• Our data came from login data and customer data, compiled by Rocinante’s data engineering team.
• The data needs to be cleaned, but it is conveniently in a PostgreSQL database.
• This is a supervised learning problem because we know which customers have churned.
• This is a binary classification problem.
• After conducting exploratory data analysis and speaking with the data engineering team, we do not see any biases in the data.
• We need to reformat some of the data and use missing data imputation for features we believe are important but have some missing data points.
• With 49 good features, we don’t believe we need to do any feature engineering.
• We have used random forests, XGBoost, and standard logistic regressions to solve classification problems.
• We will use ROC-AUC score as our evaluation metric.
• We are going to use a training-test split (80% training, 20% test) to evaluate our model.
• Let’s remove features that are statistically insignificant from our model to improve the ROC-AUC score.
• Let’s optimize the parameters within our random forests model to improve the ROC-AUC score.
• Our team believes we are acting ethically.

This process may look deceivingly linear, but data science is often a nonlinear practice. After doing all of the work in our example above, we could still end up with a model that doesn’t generalize well. It could be bad at predicting churn in new customers. Maybe we shouldn’t have assumed this problem was a binary classification problem and instead used survival regression to solve the problem. This part of the project will be filled with experimentation, and that’s totally normal.

Communication

What is the best way to communicated and circulate our results.

Our job is typically to bring our findings to the client, explain how the process was a success or failure, and explain why. Communicating technical details and explaining to non-technical audiences is important because not all of our clients have degrees in statistics.  There are three ways in which communication of technical details can be advantageous:

• It can be used to inspire confidence that the work is thorough and multiple options have been considered.
• It can highlight technical considerations or caveats that stakeholders and decision-makers should be aware of.  
• It can offer resources to learn more about specific techniques applied.
• It can provide supplemental materials to allow the findings to be replicated where possible.

We often use blog posts and articles to circulate our work. They help spread our knowledge and the lessons we learned while working on a project to peers. I encourage every data scientist to engage with the data science community by attending and speaking at meetups and conferences, publishing their work online, and extending a helping hand to other curious data scientists and analysts.

Our method of binary classification was in fact incorrect, so we ended up using survival regression to determine there are four features that impact churn: gaming platform, geographical region, days since last update, and season. Our team aggregates all of our findings into one report, detailing the specific techniques we used, caveats about the analysis, and the multiple recommendations from our team to the customer retention and acquisition team. This report is full of the nitty-gritty details that the more technical folks, such as the data engineering team, may appreciate. Our team also creates a slide deck for the less-technical audience. This deck glosses over many of the technical details of the project and focuses on recommendations for the customer retention and acquisition team.

We give a talk at a local data science meetup, going over the trials, tribulations, and triumphs of the project and sharing them with the data science community at large.

Why are we doing all of this?

I ask myself this question daily — and not in the metaphysical sense, but in the value-driven sense. Is there value in the work we have done and in the end result? I hope the answer is yes. But, let’s be honest, this is business. We don’t have three years to put together a PhD thesis-like paper. We have to move quickly and cost-effectively. Critically evaluating the value ultimately created will help you refine your approach to the next project. And, if you didn’t produce the value you’d originally hoped, then at the very least, I hope you were able to learn something and sharpen your data science skills. 

Rocinante has a better idea of how long our users will remain active on the platform based on user characteristics, and can now launch preemptive strikes in order to retain those users who look like they are about to churn. Our team eventually develops a system that alerts the customer retention and acquisition team when a user may be about to churn, and they know to reach out to that user, via email, encouraging them to try out a new feature we recently launched. Rocinante is making better data-informed decisions based on this work, and that’s great!

I hope this article will help guide your next data science project and get the wheels turning in your own mind. Maybe you will be the creator of a data science framework the world adopts! Let me know what you think about the questions, or whether I’m missing anything, in the comments below.

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5 Steps on How to Approach a New Data Science Problem

Many companies struggle to reorganize their decision making around data and implement a coherent data strategy. The problem certainly isn’t lack of data but inability to transform it into actionable insights. Here's how to do it right.

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Introduction

Data has become the new gold. 85 percent of companies are trying to be data-driven, according to last year’s survey by NewVantage Partners , and the global data science platform market is expected to reach $128.21 billion by 2022, up from $19.75 billion in 2016.

Clearly, data science is not just another buzzword with limited real-world use cases. Yet, many companies struggle to reorganize their decision making around data and implement a coherent data strategy. The problem certainly isn’t lack of data.

In the past few years alone, 90 percent of all of the world’s data has been created, and our current daily data output has reached 2.5 quintillion bytes, which is such a mind-bogglingly large number that it’s difficult to fully appreciate the break-neck pace at which we generate new data.

The real problem is the inability of companies to transform the data they have at their disposal into actionable insights that can be used to make better business decisions, stop threats, and mitigate risks.

In fact, there’s often too much data available to make a clear decision, which is why it’s crucial for companies to know how to approach a new data science problem and understand what types of questions data science can answer.

What types of questions can data science answer?

“Data science and statistics are not magic. They won’t magically fix all of a company’s problems. However, they are useful tools to help companies make more accurate decisions and automate repetitive work and choices that teams need to make,” writes Seattle Data Guy , a data-driven consulting agency.

The questions that can be answered with the help of data science fall under following categories:

• Identifying themes in large data sets : Which server in my server farm needs maintenance the most?
• Identifying anomalies in large data sets : Is this combination of purchases different from what this customer has ordered in the past?
• Predicting the likelihood of something happening : How likely is this user to click on my video?
• Showing how things are connected to one another : What is the topic of this online article?
• Categorizing individual data points : Is this an image of a cat or a mouse?

Of course, this is by no means a complete list of all questions that data science can answer. Even if it were, data science is evolving at such a rapid pace that it would most likely be completely outdated within a year or two from its publication.

Now that we’ve established the types of questions that can be reasonably expected to be answered with the help of data science, it’s time to lay down the steps most data scientists would take when approaching a new data science problem.

Step 1: Define the problem

First, it’s necessary to accurately define the data problem that is to be solved. The problem should be clear, concise, and measurable . Many companies are too vague when defining data problems, which makes it difficult or even impossible for data scientists to translate them into machine code.

Here are some basic characteristics of a well-defined data problem:

• The solution to the problem is likely to have enough positive impact to justify the effort.
• Enough data is available in a usable format.
• Stakeholders are interested in applying data science to solve the problem.

Step 2: Decide on an approach

There are many data science algorithms that can be applied to data, and they can be roughly grouped into the following families:

• Two-class classification : useful for any question that has just two possible answers.
• Multi-class classification : answers a question that has multiple possible answers.
• Anomaly detection : identifies data points that are not normal.
• Regression : gives a real-valued answer and is useful when looking for a number instead of a class or category.
• Multi-class classification as regression : useful for questions that occur as rankings or comparisons.
• Two-class classification as regression : useful for binary classification problems that can also be reformulated as regression.
• Clustering : answer questions about how data is organized by seeking to separate out a data set into intuitive chunks.
• Dimensionality reduction : reduces the number of random variables under consideration by obtaining a set of principal variables.
• Reinforcement learning algorithms : focus on taking action in an environment so as to maximize some notion of cumulative reward.

Step 3: Collect data

With the problem clearly defined and a suitable approach selected, it’s time to collect data. All collected data should be organized in a log along with collection dates and other helpful metadata.

It’s important to understand that collected data is seldom ready for analysis right away. Most data scientists spend much of their time on data cleaning , which includes removing missing values, identifying duplicate records, and correcting incorrect values.

Step 4: Analyze data

The next step after data collection and cleanup is data analysis. At this stage, there’s a certain chance that the selected data science approach won’t work. This is to be expected and accounted for. Generally, it’s recommended to start with trying all the basic machine learning approaches as they have fewer parameters to alter.

There are many excellent open source data science libraries that can be used to analyze data. Most data science tools are written in Python, Java, or C++.

<blockquote><p>“Tempting as these cool toys are, for most applications the smart initial choice will be to pick a much simpler model, for example using scikit-learn and modeling techniques like simple logistic regression,” – advises Francine Bennett, the CEO and co-founder of Mastodon C.</p></blockquote>

Step 5: Interpret results

After data analysis, it’s finally time to interpret the results. The most important thing to consider is whether the original problem has been solved. You might discover that your model is working but producing subpar results. One way how to deal with this is to add more data and keep retraining the model until satisfied with it.

Most companies today are drowning in data. The global leaders are already using the data they generate to gain competitive advantage, and others are realizing that they must do the same or perish. While transforming an organization to become data-driven is no easy task, the reward is more than worth the effort.

The 5 steps on how to approach a new data science problem we’ve described in this article are meant to illustrate the general problem-solving mindset companies must adopt to successfully face the challenges of our current data-centric era.

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Data Science vs. Software Engineering: Key Differences Explained

Author: University of North Dakota July 15, 2024

In recent decades, technology has evolved at an astonishing pace, giving rise to extraordinary inventions, from smartphones to artificial intelligence and even self-driving cars.

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These innovations have truly revolutionized our lives, making everyday tasks more efficient and expanding the horizons of what is possible.

However, amidst this technological progress, there is a slight downside: the sheer number of specialized fields and disciplines can make choosing a career path a daunting challenge for students. For example, this problem is evident in fields like data science and software engineering. Both offer exciting opportunities and share some overlapping skills, yet they are fundamentally different in their focus and applications. 

As students navigate their educational and career choices, understanding these differences becomes crucial. So read on as we help you make an informed decision by comparing data science vs. software engineering and seeing which path is the best fit for you.

What is Data Science?

Data science is an interdisciplinary field dedicated to extracting valuable insights from data through a combination of analytical methods, statistical techniques and advanced computational tools. It focuses on addressing complex business problems by utilizing techniques such as machine learning, deep learning and reinforcement learning.

The field operates around a data lifecycle, where specialized professionals handle various stages of data management: capturing, maintaining, processing, analyzing and communicating information. Data scientists, analysts and engineers are crucial in this process, as they help organizations enhance operations, innovate products and services, uncover groundbreaking discoveries and mitigate risks.

What is Software Engineering?

Software engineering is a field centered on the creation, development and maintenance of software applications and computer programs. Nearly every industry and individual depends on software for daily operations, from the operating systems on phones and laptops to applications like internet browsers, Microsoft Word and Gmail. Software engineers are responsible for designing, building and updating these programs to ensure they function smoothly and securely. 

Careers in software engineering often involve specializations, such as developing user interfaces that people interact with or designing cybersecurity solutions to protect users from malware and other threats.

Differences Between Data Science and Software Engineering

At first glance, these two fields are similar, both revolving around the use of technology and programming. However, they diverge significantly in focus, methodology and application. To better understand the distinctions between these two fields, let's compare software engineering vs. data science from the education required to the salaries you can expect to earn.

Education Path

To become a data scientist, a bachelor's degree in data science , statistics, computer science or a related field is typically required. These programs cover foundational topics such as programming, statistical analysis, machine learning and data visualization. 

Considering the aforementioned rapid evolution of technology, continuous learning is essential to keep pace with the latest tools and methodologies. Therefore, many data scientists further their education by pursuing a master's degree in data science , which provides in-depth knowledge in specialized areas like advanced machine learning, predictive modeling and big data analytics. Certifications in data science and related fields can also add value and enhance a professional's credentials.

On the other hand, software engineers generally begin their careers with a bachelor's degree in computer science , software engineering, mathematics or a similar discipline. These programs focus on software development principles, algorithms, data structures and various programming languages.

Just like in data science, ongoing education is necessary. So, certifications in specific programming languages, development frameworks or methodologies significantly strengthen a software engineer's skill set and marketability.

Essential Skills

Proficiency in programming languages like Python and SQL is crucial for both data science and software engineering, as these languages are widely used for data manipulation, analysis and software development. Additionally, the ability to identify issues, analyze potential solutions and implement effective strategies is essential in both fields. 

Strong analytical skills are also necessary to interpret complex data, derive meaningful insights and develop innovative solutions to technical challenges. However, beyond these shared skills, each field requires specialized skills that ensure professionals can fulfill their specific responsibilities.

For example, data scientists need to manage and analyze large datasets, build predictive models and generate actionable insights. Therefore, they must be skilled in:

• Understanding and applying statistical methods to interpret data and identify trends
• Designing and implementing algorithms that enable systems to learn from data and make predictions
• Creating visual representations of data to communicate findings effectively to stakeholders
• Extracting useful information from large datasets through various techniques
• Utilizing tools and frameworks like Hadoop and Spark to handle and process massive amounts of data

However, software engineers focus on developing and maintaining software applications and this requires a different set of skills, which include:

• Mastery of multiple programming languages, such as Java, C++ and JavaScript, to develop software solutions
• Familiarity with development frameworks like Agile and Scrum that promote efficient and collaborative project management
• Designing scalable and efficient software systems and architectures
• Using version control tools to manage and track changes in codebases
• Identifying and fixing bugs to ensure software reliability and performance

Roles and Responsibilities

Both data science and software engineering encompass a wide range of roles, each with unique responsibilities and skill requirements.

In data science, key roles include:

• Data scientist: Focuses on extracting insights from complex datasets, developing predictive models and applying statistical analysis to support business decisions.
• Data analyst: Primarily responsible for cleaning and interpreting data, creating detailed reports to identify trends and informing strategic planning.
• Machine learning engineer: Designs and implements algorithms that enable systems to learn and improve from experience, often leading to automation and efficiency enhancements.

In software engineering, important roles include:

• Software developer: Writes code and creates software applications that meet user needs and business objectives.
• Software architect: Designs the overarching structure of software systems, ensuring scalability, efficiency and maintainability.
• Quality assurance engineer: Focuses on testing software to identify and resolve defects, ensuring the final product meets quality standards.

General Responsibilities in Data Science

• Developing statistical models to interpret complex data
• Automating processes and tasks through software engineering techniques
• Conducting data analysis to identify trends and patterns
• Collaborating with other departments to understand data needs and provide actionable insights
• Preparing detailed reports and visualizations to communicate findings to stakeholders

General Responsibilities in Software Engineering

• Designing various components of software
• Documenting software development processes and methodologies
• Implementing software solutions based on user requirements and feedback
• Conducting testing and debugging to ensure software quality
• Managing version control using tools like Git
• Collaborating with cross-functional teams to integrate software solutions with other systems and platforms

These roles and responsibilities illustrate the diversity and specialization within both fields, highlighting how professionals contribute to developing reliable, high-performing solutions across multiple industries.

Career Opportunities

Both data science and software engineering offer excellent career opportunities across industries such as technology, finance, healthcare and e-commerce, among others. The demand for these professionals is growing rapidly, with significant potential for career advancement and specialization.

Employment of data scientists is projected to grow by 35% from 2022 to 2032, reflecting the increasing reliance on data-driven decision-making across sectors. In comparison, the overall employment of software developers, quality assurance analysts and testers is expected to increse by 25% over the same period, driven by the continuous expansion of digital technologies and the need for robust software solutions. Both fields promise strong job markets with diverse opportunities for those equipped with the necessary skills and expertise.

Average Salaries

Both data science and software engineering are lucrative fields with substantial earning potential, particularly for those who advance to senior or specialized positions. 

In the United States, the average annual salary for a data scientist is approximately $122,738 , ranging from $37,500 to $196,500 per year. Software engineers tend to have higher average salaries, with an average annual pay of $147,524 . Their wages typically range from $63,500 to $205,500 per year. This variation accounts for different specializations as well as differences in industry demand and geographic location.

Data Science vs. Software Engineering: Which One Should You Choose?

Data science and software engineering both have promising growth prospects and lucrative salaries, but it's crucial to align your choice with what you genuinely enjoy and excel at. So, focus on your personal interests, strengths and career goals. 

If you enjoy analyzing data, finding patterns and using statistical methods to derive insights, data science might be the right path for you. On the other hand, if you are passionate about coding, designing software systems and developing applications, software engineering could be your ideal career.

Conclusion 

All in all, the main difference between data science and software engineering is that the first focuses on analyzing and interpreting complex data, while the latter centers on designing and developing software systems. However, both fields offer exceptional career opportunities and significant potential for growth.

At the University of North Dakota (UND), you have the flexibility to pursue either field and we provide the education and support needed to help you succeed. So, whether you're drawn to data analysis or software development, UND is here to guide you toward a successful and fulfilling career. Success is just a degree away!

Can someone transition from data science to software engineering or vice versa? ( Open this section)

Yes, transitioning from data science to software engineering or vice versa, is possible due to their overlapping skill sets in programming and problem-solving. However, additional training or education may be necessary to fully switch fields and master the specific tools and methodologies unique to each discipline.

Which field offers better career opportunities: data science or software engineering? ( Open this section)

Both fields offer excellent career opportunities, with high demand and strong growth projections; the best choice depends on your interests and strengths in either data analysis or software development.

Can someone pursue a career in both data science and software engineering simultaneously? ( Open this section)

Yes, it is possible to pursue careers in both fields simultaneously, especially in roles that integrate data analysis with software development, though it requires a strong skill set in both areas and effective time management.

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Title: dart-math: difficulty-aware rejection tuning for mathematical problem-solving.

Abstract: Solving mathematical problems requires advanced reasoning abilities and presents notable challenges for large language models. Previous works usually synthesize data from proprietary models to augment existing datasets, followed by instruction tuning to achieve top-tier results. However, our analysis of these datasets reveals severe biases towards easy queries, with frequent failures to generate any correct response for the most challenging queries. Hypothesizing that difficult queries are crucial to learn complex reasoning, we propose Difficulty-Aware Rejection Tuning (DART), a method that allocates difficult queries more trials during the synthesis phase, enabling more extensive training on difficult samples. Utilizing DART, we have created new datasets for mathematical problem-solving that focus more on difficult queries and are substantially smaller than previous ones. Remarkably, our synthesis process solely relies on a 7B-sized open-weight model, without reliance on the commonly used proprietary GPT-4. We fine-tune various base models on our datasets ranging from 7B to 70B in size, resulting in a series of strong models called DART-MATH. In comprehensive in-domain and out-of-domain evaluation on 6 mathematical benchmarks, DART-MATH outperforms vanilla rejection tuning significantly, being superior or comparable to previous arts, despite using much smaller datasets and no proprietary models. Furthermore, our results position our synthetic datasets as the most effective and cost-efficient publicly available resources for advancing mathematical problem-solving.

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• What is Mechanical Engineering?

The essence of mechanical engineering is problem solving. MEs combine creativity, knowledge and analytical tools to complete the difficult task of shaping an idea into reality.

Mechanical engineering is one of the broadest engineering disciplines—offering opportunities to specialize in areas such as robotics, aerospace, automotive engineering, HVAC (heating, ventilation, and air conditioning), biomechanics, and more. Mechanical engineers design, develop, build, and test. They deal with anything that moves, from components to machines to the human body. The work of mechanical engineers plays a crucial role in shaping the technology and infrastructure that drive our modern world.

What Is Mechanical Engineering?

Technically, mechanical engineering is the application of the principles and problem-solving techniques of engineering from design to manufacturing to the marketplace for any object. Mechanical engineers analyze their work using the principles of motion, energy, and force—ensuring that designs function safely, efficiently, and reliably, all at a competitive cost.

Mechanical engineers make a difference. That's because mechanical engineering careers center on creating technologies to meet human needs. Virtually every product or service in modern life has probably been touched in some way by a mechanical engineer to help humankind.

This includes solving today's problems and creating future solutions in health care, energy, transportation, world hunger, space exploration, climate change, and more.

Being ingrained in many challenges and innovations across many fields means a mechanical engineering education is versatile. To meet this broad demand, mechanical engineers may design a component, a machine, a system, or a process. This ranges from the macro to the micro, from the largest systems like cars and satellites to the smallest components like sensors and switches. Anything that needs to be manufactured—indeed, anything with moving parts—needs the expertise of a mechanical engineer .

What do mechanical engineers do?

Mechanical engineering combines creativity, knowledge and analytical tools to complete the difficult task of shaping an idea into reality.

This transformation happens at the personal scale, affecting human lives on a level we can reach out and touch like robotic prostheses. It happens on the local scale, affecting people in community-level spaces, like with agile interconnected microgrids . And it happens on bigger scales, like with advanced power systems , through engineering that operates nationwide or across the globe.

Mechanical engineers have an enormous range of opportunity and their education mirrors this breadth of subjects. Students concentrate on one area while strengthening analytical and problem-solving skills applicable to any engineering situation. Mechanical engineers work on a wide range of projects, from designing engines, power plants, and robots to developing heating and cooling systems, manufacturing processes, and even nanotechnology.

Mechanical Engineering Disciplines

Disciplines within the mechanical engineering field include but are not limited to:

• Autonomous Systems
• Biotechnology
• Computer Aided Design (CAD)
• Control Systems
• Cyber security
• Human health
• Manufacturing and additive manufacturing
• materials science
• Nanotechnology
• Production planning
• Structural analysis

Technology itself has also shaped how mechanical engineers work and the suite of tools has grown quite powerful in recent decades. Computer-aided engineering (CAE) is an umbrella term that covers everything from typical CAD techniques to computer-aided manufacturing to computer-aided engineering, involving finite element analysis (FEA) and computational fluid dynamics (CFD). These tools and others have further broadened the horizons of mechanical engineering.

What careers are there in mechanical engineering?

Society depends on mechanical engineering. The need for this expertise is great in so many fields, and as such, there is no real limit for the freshly minted mechanical engineer. Jobs are always in demand, particularly in the automotive, aerospace, electronics, biotechnology, and energy industries.

Mechanical Engineering Job Types

Here are a handful of mechanical engineering fields .

Mechanical engineers play vital roles in the aerospace industry, contributing to various aspects of aircraft and spacecraft design, development, and maintenance.

In statics , research focuses on how forces are transmitted to and throughout a structure. Once a system is in motion, mechanical engineers look at dynamics , or what velocities, accelerations and resulting forces come into play. Kinematics then examines how a mechanism behaves as it moves through its range of motion.

Materials science delves into determining the best materials for different applications. A part of that is materials strength —testing support loads, stiffness, brittleness and other properties—which is essential for many construction, automobile, and medical materials.

How energy gets converted into useful power is the heart of thermodynamics , as well as determining what energy is lost in the process. One specific kind of energy, heat transfer , is crucial in many applications and requires gathering and analyzing temperature data and distributions.

Fluid mechanics , which also has a variety of applications, looks at many properties including pressure drops from fluid flow and aerodynamic drag forces.

Manufacturing is an important step in mechanical engineering. Within the field, researchers investigate the best processes to make manufacturing more efficient. Laboratory methods focus on improving how to measure both thermal and mechanical engineering products and processes. Likewise, machine design develops equipment-scale processes while electrical engineering focuses on circuitry. All this equipment produces vibrations , another field of mechanical engineering, in which researchers study how to predict and control vibrations.

Engineering economics makes mechanical designs relevant and usable in the real world by estimating manufacturing and life cycle costs of materials, designs, and other engineered products.

What skills do mechanical engineers need?

The essence of engineering is problem solving. With this at its core, mechanical engineering also requires applied creativity—a hands on understanding of the work involved—along with strong interpersonal skills like networking, leadership, and conflict management. Creating a product is only part of the equation; knowing how to work with people, ideas, data, and economics fully makes a mechanical engineer.

Here are ten essential skills for mechanical engineers to possess:

Technical Knowledge: A strong foundation in physics, mathematics, and mechanics is crucial. Understanding principles like thermodynamics, fluid mechanics, materials science, and structural analysis forms the backbone of mechanical engineering.

Problem-Solving: Mechanical engineers often encounter complex problems that require analytical thinking and creative solutions. The ability to break down problems and develop innovative solutions is highly valuable.

Design and CAD: Proficiency in computer-aided design (CAD) software is essential for creating, analyzing, and optimizing designs. Knowledge of software like SolidWorks, AutoCAD, or similar programs is valuable.

Critical Thinking: Assessing risks, evaluating different design options, and making decisions based on data and analysis are critical skills for mechanical engineers.

Communication: Being able to communicate technical information clearly, whether in written reports, presentations, or discussions with team members or clients, is vital for success in this field.

Project Management: Managing projects, including budgeting, scheduling, and coordinating with teams, suppliers, and clients, is often part of a mechanical engineer's role.

Hands-on Application: Practical skills in building prototypes, conducting experiments, and testing designs are valuable. Having a good understanding of manufacturing processes and techniques is beneficial.

Continuous Learning/Improvement: Given the rapid advancements in technology and techniques, a willingness to learn and adapt to new tools, methodologies, and industry trends is crucial for staying competitive.

Teamwork: Mechanical engineers often work in multidisciplinary teams. The ability to collaborate effectively with professionals from various backgrounds is essential.

Ethical Standards: Upholding ethical standards and understanding the broader impact of engineering solutions on society and the environment is increasingly important for modern mechanical engineers.

Developing a balance of technical expertise, problem-solving capabilities, and soft skills is key to becoming a successful mechanical engineer.

What tasks do mechanical engineers do?

Careers in mechanical engineering call for a variety of tasks.

• Conceptual design
• Presentations and report writing
• Multidisciplinary teamwork
• Concurrent engineering
• Benchmarking the competition
• Project management
• Prototyping
• Measurements
• Data Interpretation
• Developmental design
• Analysis (FEA and CFD)
• Working with suppliers
• Customer service

How much do mechanical engineers earn?

Like careers in many other engineering fields, mechanical engineers are well paid. Compared to other fields, mechanical engineers earn well above average throughout each stage of their careers. According to the U.S. Bureau of Labor Statistics, the mean salary for a mechanical engineer is $105,220 , with the top ten percent earning close to $157,470 .

See additional engineering salary information .

The future of mechanical engineering

Breakthroughs in materials and analytical tools have opened new frontiers for mechanical engineers. Nanotechnology, biotechnology, composites, computational fluid dynamics (CFD), and acoustical engineering have all expanded the mechanical engineering toolbox.

Nanotechnology allows for the engineering of materials on the smallest of scales. With the ability to design and manufacture down to the elemental level, the possibilities for objects grows immensely. Composites are another area where the manipulation of materials allows for new manufacturing opportunities. By combining materials with different characteristics in innovative ways, the best of each material can be employed and new solutions found. CFD gives mechanical engineers the opportunity to study complex fluid flows analyzed with algorithms. This allows for the modeling of situations that would previously have been impossible. Acoustical engineering examines vibration and sound, providing the opportunity to reduce noise in devices and increase efficiency in everything from biotechnology to architecture.

How do I become a mechanical engineer?

There are several paths you can take to a career in mechanical engineering . Tomorrow needs MEs who are prepared to make a difference in the world to solve challenges in healthcare, energy, transportation, space exploration, climate change, and more.

Most entry-level mechanical engineering positions require at least a bachelor's degree in mechanical engineering or mechanical engineering technology. Positions that are related to national defense may need a security clearance and a US citizenship may be required for certain types and levels of clearances.

In high school, focus on classes in math and physics. Other science courses can also be helpful. Research colleges and universities offering an accredited mechanical engineering degree program. Visit the schools you are interested in and apply early. Become a mechanical engineer.

Mechanical Engineering at Michigan Tech

We are committed to our mission of hands-on education of our mechanical engineering students, by world-class faculty, through innovative teaching, mentoring, and knowledge creation.

Mechanical Engineering Degrees

The bachelor's degree in mechanical engineering at Michigan Tech offers undergraduate students many unique, hands-on learning opportunities:

Undergraduate Research Opportunities

Undergraduate research opportunities are plentiful. Our department offers undergraduate students numerous opportunities in research, hands-on experience, and real-world client work. Research projects often require help from students for running simulations, taking data, analyzing results, etc. These opportunities may even be paid, depending on the availability of funds on the particular project. Take advantage of over 50,000 square feet of labs and computer centers, in the 13-story R. L. Smith Mechanical Engineering-Engineering Mechanics Building.

Real-World Experience

Get ready to contribute on the job from day one. Our students benefit from hands-on experiences ranging from our senior capstone design program to our enterprise teams to internships/co-ops . As a mechanical engineer, you can make a difference in the world by using the latest technologies to help solve today's grand challenges.

ABET Accreditation

Our undergraduate mechanical engineering program is ABET Accredited . ABET accreditation is a significant achievement. We have worked hard to ensure that our program meets the quality standards set by the profession. And, because it requires comprehensive, periodic evaluations, ABET accreditation demonstrates our continuing commitment to the quality of our program—both now and in the future.

Prepare for Graduate Study

Our undergraduate program in mechanical engineering prepares you for advanced study in the field. Earn an MS degree in mechanical engineering , an MS degree in engineering mechanics , or a PhD degree in mechanical Engineering–engineering mechanics .

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GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses

Following the research path from gpt, gpt-2, and gpt-3, our deep learning approach leverages more data and more computation to create increasingly sophisticated and capable language models., we spent 6 months making gpt-4 safer and more aligned. gpt-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than gpt-3.5 on our internal evaluations., safety & alignment.

Training with human feedback We incorporated more human feedback, including feedback submitted by ChatGPT users, to improve GPT-4’s behavior. We also worked with over 50 experts for early feedback in domains including AI safety and security.

Continuous improvement from real-world use We’ve applied lessons from real-world use of our previous models into GPT-4’s safety research and monitoring system. Like ChatGPT, we’ll be updating and improving GPT-4 at a regular cadence as more people use it.

GPT-4-assisted safety research GPT-4’s advanced reasoning and instruction-following capabilities expedited our safety work. We used GPT-4 to help create training data for model fine-tuning and iterate on classifiers across training, evaluations, and monitoring .

Built with GPT-4

We’ve collaborated with organizations building innovative products with GPT-4.

GPT-4 deepens the conversation on Duolingo.

Be My Eyes uses GPT-4 to transform visual accessibility.

Stripe leverages GPT-4 to streamline user experience and combat fraud.

Morgan Stanley wealth management deploys GPT-4 to organize its vast knowledge base.

Khan Academy explores the potential for GPT-4 in a limited pilot program.

How Iceland is using GPT-4 to preserve its language.

More on GPT-4

Research GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning. View GPT-4 research

Infrastructure GPT-4 was trained on Microsoft Azure AI supercomputers. Azure’s AI-optimized infrastructure also allows us to deliver GPT-4 to users around the world.

Limitations GPT-4 still has many known limitations that we are working to address, such as social biases, hallucinations, and adversarial prompts. We encourage and facilitate transparency, user education, and wider AI literacy as society adopts these models. We also aim to expand the avenues of input people have in shaping our models.

Availability GPT-4 is available on ChatGPT Plus and as an API for developers to build applications and services.

View contributions

We’re excited to see how people use GPT-4 as we work towards developing technologies that empower everyone.

Advertisement

Chaos and Confusion: Tech Outage Causes Disruptions Worldwide

Airlines, hospitals and people’s computers were affected after CrowdStrike, a cybersecurity company, sent out a flawed software update.

• Share full article

By Adam Satariano Paul Mozur Kate Conger and Sheera Frenkel

• July 19, 2024

Airlines grounded flights. Operators of 911 lines could not respond to emergencies. Hospitals canceled surgeries. Retailers closed for the day. And the actions all traced back to a batch of bad computer code.

A flawed software update sent out by a little-known cybersecurity company caused chaos and disruption around the world on Friday. The company, CrowdStrike , based in Austin, Texas, makes software used by multinational corporations, government agencies and scores of other organizations to protect against hackers and online intruders.

But when CrowdStrike sent its update on Thursday to its customers that run Microsoft Windows software, computers began to crash.

The fallout, which was immediate and inescapable, highlighted the brittleness of global technology infrastructure. The world has become reliant on Microsoft and a handful of cybersecurity firms like CrowdStrike. So when a single flawed piece of software is released over the internet, it can almost instantly damage countless companies and organizations that depend on the technology as part of everyday business.

“This is a very, very uncomfortable illustration of the fragility of the world’s core internet infrastructure,” said Ciaran Martin, the former chief executive of Britain’s National Cyber Security Center and a professor at the Blavatnik School of Government at Oxford University.

A cyberattack did not cause the widespread outage, but the effects on Friday showed how devastating the damage can be when a main artery of the global technology system is disrupted. It raised broader questions about CrowdStrike’s testing processes and what repercussions such software firms should face when flaws in their code cause major disruptions.

How a Software Update Crashed Computers Around the World

Here’s a visual explanation for how a faulty software update crippled machines.

How the airline cancellations rippled around the world (and across time zones)

Share of canceled flights at 25 airports on Friday

50% of flights

Ai r po r t

Bengalu r u K empeg o wda

Dhaka Shahjalal

Minneapolis-Saint P aul

Stuttga r t

Melbou r ne

Be r lin B r anden b urg

London City

Amsterdam Schiphol

Chicago O'Hare

Raleigh−Durham

B r adl e y

Cha r lotte

Reagan National

Philadelphia

1:20 a.m. ET

CrowdStrike’s stock price so far this year

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