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GE3171 – Problem Solving And Python Programming Laboratory Syllabus Regulation 2021 Anna University

January 17, 2024 by Raju

Anna University, Subject code – GE3171, deals with the B.E civil Engineering Semester – I Problem Solving And Python Programming Laboratory Syllabus regulation 2021 relating to affiliated institutions. From here, Students can get assistance in preparing notes to excel in academic performance.

We include every topic of the Problem Solving And Python Programming Laboratory Syllabus, to understand the subject very well. It will help you to improve your idea of syllabus of GE3171 – Problem Solving And Python Programming Laboratory Syllabus on your finger tips to go ahead in a clear path of preparation. In this following article Problem Solving And Python Programming Laboratory Syllabus , will help you, Hope you share with your friends.

If you want to know more about the syllabus of B.E Civil Engineering connected to an affiliated institution’s under four-year undergraduate degree programme. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.E Civil Engineering Syllabus Anna University, Regulation 2021.

Aim Of Concept:

• To understand the problem-solving approaches.
• To learn the basic programming constructs in Python.
• To practice various computing strategies for Python-based solutions to real-world problems.
• To use Python data structures – lists, tuples, dictionaries.
• To do input/output with files in Python.

GE3171 – Problem Solving And Python Programming Laboratory Syllabus

Experiments:

Note: The examples suggested in each experiment are only indicative. The lab instructor is expected to design other problems on similar lines. The Examination shall not be restricted to the sample experiments listed here.

• Identification and solving of simple real-life or scientific or technical problems, and developing flow charts for the same. (Electricity Billing, Retail shop billing, Sin series, weight of a motorbike, Weight of a steel bar, compute Electrical Current in Three Phase AC Circuit, etc.)
• Python programming using simple statements and expressions (exchange the values of two variables, circulate the values of n variables, distance between two points).
• Scientific problems using Conditionals and Iterative loops. (Number series, Number Patterns, pyramid pattern)
• Implementing real-time/technical applications using Lists and tuples. (Items present in a library/Components of a car/ Materials required for construction of a building –operations of list & tuples)
• Implementing real-time/technical applications using Sets and dictionaries. (Language, components of an automobile, Elements of a civil structure, etc.- operations of Sets & Dictionaries)
• Implementing programs using Functions. (Factorial, largest number in a list, area of shape)
• Implementing programs using Strings. (reverse, palindrome, character count, replacing characters)
• Implementing programs using written modules and Python Standard Libraries (pandas, numpy. Matplotlib, scipy)
• Implementing real-time/technical applications using File handling. (copy from one file to another, word count, longest word)
• Implementing real-time/technical applications using Exception handling. (divide by zero error, voter’s age validity, student mark range validation)
• Exploring Pygame tool.
• Developing a game activity using Pygame like bouncing ball, car race, etc.

Text Books:

• Allen B. Downey, “Think Python: How to Think like a Computer Scientist”, 2nd Edition, O’Reilly Publishers, 2016.
• Karl Beecher, “Computational Thinking: A Beginner’s Guide to Problem-Solving and Programming”, 1st Edition, BCS Learning & Development Limited, 2017.

References:

• Paul Deitel and Harvey Deitel, “Python for Programmers”, Pearson Education, 1st Edition, 2021.
• G Venkatesh and Madhavan Mukund, “Computational Thinking: A Primer for Programmers and Data Scientists”, 1st Edition, Notion Press, 2021.
• John V Guttag, “Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data‘‘, Third Edition, MIT Press, 2021
• Eric Matthes, “Python Crash Course, A Hands-on Project-Based Introduction to Programming”, 2nd Edition, No Starch Press, 2019.
• https://www.python.org/
• Martin C. Brown, “Python: The Complete Reference”, 4th Edition, Mc-Graw Hill, 2018.

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• CY3151 – Engineering Chemistry
• GE3151 – Problem Solving and Python Programming
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Problem Solving and Python Programming Laboratory - GE3171

Lab manual 3.

Experiments

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GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Regulation 2021 Anna University

• Post author By Mr. Padeepz
• Post date October 31, 2021
• No Comments on GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Regulation 2021 Anna University

GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY

GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Regulation 2021 Anna University free download. PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Syllabus GE3171 pdf download free.

COURSE OBJECTIVES: 􀁸 To understand the problem solving approaches. 􀁸 To learn the basic programming constructs in Python. 􀁸 To practice various computing strategies for Python-based solutions to real world problems. 􀁸 To use Python data structures – lists, tuples, dictionaries. 􀁸 To do input/output with files in Python.

EXPERIMENTS: GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Note: The examples suggested in each experiment are only indicative. The lab instructor is expected to design other problems on similar lines. The Examination shall not be restricted to the sample experiments listed here. 1. Identification and solving of simple real life or scientific or technical problems, and developing flow charts for the same. (Electricity Billing, Retail shop billing, Sin series, weight of a motorbike, Weight of a steel bar, compute Electrical Current in Three Phase AC Circuit, etc.) 2. Python programming using simple statements and expressions (exchange the values of two variables, circulate the values of n variables, distance between two points). 3. Scientific problems using Conditionals and Iterative loops. (Number series, Number Patterns, pyramid pattern)

4. Implementing real-time/technical applications using Lists, Tuples. (Items present in a library/Components of a car/ Materials required for construction of a building –operations of list & tuples) 5. Implementing real-time/technical applications using Sets, Dictionaries. (Language, components of an automobile, Elements of a civil structure, etc.- operations of Sets &  Dictionaries) GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY 6. Implementing programs using Functions. (Factorial, largest number in a list, area of shape) 7. Implementing programs using Strings. (reverse, palindrome, character count, replacing characters) 8. Implementing programs using written modules and Python Standard Libraries (pandas,  numpy. Matplotlib, scipy) 9. Implementing real-time/technical applications using File handling. (copy from one file to  another, word count, longest word) 10. Implementing real-time/technical applications using Exception handling. (divide by zero error, voter’s age validity, student mark range validation) 11. Exploring Pygame tool. 12. Developing a game activity using Pygame like bouncing ball, car race etc.

TOTAL: 60 PERIODS

COURSE OUTCOMES:

On completion of the course, students will be able to: CO1: Develop algorithmic solutions to simple computational problems CO2: Develop and execute simple Python programs. CO3: Implement programs in Python using conditionals and loops for solving problems. CO4: Deploy functions to decompose a Python program. CO5: Process compound data using Python data structures. CO6: Utilize Python packages in developing software applications.

TEXT BOOKS: GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY

1. Allen B. Downey, “Think Python : How to Think like a Computer Scientist”, 2nd Edition, O’Reilly Publishers, 2016. 2. Karl Beecher, “Computational Thinking: A Beginner’s Guide to Problem Solving and Programming”, 1st Edition, BCS Learning & Development Limited, 2017.

REFERENCES:

1. Paul Deitel and Harvey Deitel, “Python for Programmers”, Pearson Education, 1st Edition, 2021. 2. G Venkatesh and Madhavan Mukund, “Computational Thinking: A Primer for Programmers and Data Scientists”, 1st Edition, Notion Press, 2021. 3. John V Guttag, “Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data‘‘, Third Edition, MIT Press, 2021 4. Eric Matthes, “Python Crash Course, A Hands – on Project Based Introduction to Programming”, 2nd Edition, No Starch Press, 2019. 5. https://www.python.org/ 6. Martin C. Brown, “Python: The Complete Reference”, 4th Edition, Mc-Graw Hill, 2018.

GE3171 Syllabus PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY Click Here To Download

Lab Manual In Padeepz App – Click Here To Download

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GE3171 Problem Solving and Python Programming Laboratory Manual (Lab Manual)

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GE3171: Problem Solving and Python Programming Laboratory syllabus for AI&DS 2021 regulation

• Updated on Mar 22, 2023
• By InI Labs TN

Problem Solving and Python Programming Laboratory detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Artificial Intelligence & Data Science 1st Sem scheme and its subjects, do visit AI&DS 1st Sem 2021 regulation scheme . The detailed syllabus of problem solving and python programming laboratory is as follows.

Problem Solving and Python Programming Laboratory

Course Objectives:

• To understand the problem solving approaches.
• To learn the basic programming constructs in Python.
• To practice various computing strategies for Python-based solutions to real world problems.
• To use Python data structures – lists, tuples, dictionaries.
• To do input/output with files in Python.

Experiments:

Note: The examples suggested in each experiment are only indicative. The lab instructor is expected to design other problems on similar lines. The Examination shall not be restricted to the sample experiments listed here.

• Identification and solving of simple real life or scientific or technical problems, and developing flow charts for the same. (Electricity Billing, Retail shop billing, Sin series, weight of a motorbike, Weight of a steel bar, compute Electrical Current in Three Phase AC Circuit, etc.)
• Python programming using simple statements and expressions (exchange the values of two variables, circulate the values of n variables, distance between two points).
• Scientific problems using Conditionals and Iterative loops. (Number series, Number Patterns, pyramid pattern)
• Implementing real-time/technical applications using Lists, Tuples. (Items present in a library/Components of a car/ Materials required for construction of a building -operations of list & tuples)
• Implementing real-time/technical applications using Sets, Dictionaries. (Language, components of an automobile, Elements of a civil structure, etc.- operations of Sets & Dictionaries)
• Implementing programs using Functions. (Factorial, largest number in a list, area of shape)
• Implementing programs using Strings. (reverse, palindrome, character count, replacing characters)
• Implementing programs using written modules and Python Standard Libraries (pandas, numpy. Matplotlib, scipy)
• Implementing real-time/technical applications using File handling. (copy from one file to another, word count, longest word)
• Implementing real-time/technical applications using Exception handling. (divide by zero error, voters age validity, student mark range validation)
• Exploring Pygame tool.
• Developing a game activity using Pygame like bouncing ball, car race etc.

Course Outcomes:

On completion of the course, students will be able to:

• Develop algorithmic solutions to simple computational problems
• Develop and execute simple Python programs.
• Implement programs in Python using conditionals and loops for solving problems..
• Deploy functions to decompose a Python program.
• Process compound data using Python data structures.
• Utilize Python packages in developing software applications.

Text Books:

• Allen B. Downey, Think Python : How to Think like a Computer Scientist, 2nd Edition, OReilly Publishers, 2016.
• Karl Beecher, Computational Thinking: A Beginner’s Guide to Problem Solving and Programming, 1st Edition, BCS Learning & Development Limited, 2017.

Reference Books:

• Paul Deitel and Harvey Deitel, Python for Programmers, Pearson Education, 1st Edition, 2021.
• G Venkatesh and Madhavan Mukund, Computational Thinking: A Primer for Programmers and Data Scientists, 1st Edition, Notion Press, 2021.
• John V Guttag, “Introduction to Computation and Programming Using Python: With Applications to Computational Modeling and Understanding Data, Third Edition, MIT Press, 2021
• Eric Matthes, Python Crash Course, A Hands – on Project Based Introduction to Programming, 2nd Edition, No Starch Press, 2019.
• https://www.python.org/
• Martin C. Brown, Python: The Complete Reference, 4th Edition, Mc-Graw Hill, 2018.

For detailed syllabus of all other subjects of Artificial Intelligence & Data Science, 2021 regulation curriculum do visit AI&DS 1st Sem subject syllabuses for 2021 regulation .

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.

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