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.