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GE3151Actively Used

Problem Solving and Python Programming

This course is part of the B.E. Computer Science Engineering curriculum under Anna University Regulation 2021. The knowledge from this course continues to be actively applied in professional software development.

Semester 1 (First Year)
3 Credits
45 Lecture Hours

Course Overview

  • UniversityAnna University
  • Regulation2021
  • Semester1
  • Credits3
  • TypeCore
  • Units5

Course Objectives

1

To understand computational thinking and problem solving

2

To learn Python data types, expressions, and statements

3

To understand control flow, functions, and strings

4

To learn lists, tuples, and dictionaries

5

To understand file handling and modules

Syllabus

Detailed unit-wise breakdown of the course curriculum as per Anna University Regulation 2021.

1

COMPUTATIONAL THINKING AND PROBLEM SOLVING

9 Hours
Fundamentals of ComputingIdentification of computational problemsAlgorithmsBuilding blocks – statements, state, control flow, functionsNotation – pseudo code, flow chartAlgorithmic problem solvingStrategies – iteration, recursionIllustrative problems – minimum in listInserting card in sorted listTowers of Hanoi
2

DATA TYPES, EXPRESSIONS, STATEMENTS

9 Hours
Python interpreter and interactive modeDebuggingValues and types – int, float, boolean, string, listVariablesExpressions and statementsTuple assignmentPrecedence of operatorsCommentsIllustrative programs – exchanging valuesCirculating valuesDistance between two points
3

CONTROL FLOW, FUNCTIONS, STRINGS

9 Hours
Conditionals – if, if-else, if-elif-elseIteration – while, for, break, continue, passFruitful functions – return values, parametersLocal and global scopeFunction compositionRecursionStrings – slices, immutability, methodsLists as arraysIllustrative programs – square root, gcdLinear search, binary search
4

LISTS, TUPLES, DICTIONARIES

9 Hours
Lists – operations, slices, methodsList loop and mutabilityAliasing and cloningList parametersTuples – assignment, as return valueDictionaries – operations and methodsAdvanced list processingList comprehensionIllustrative programs – sorting, histogramStudent marks statement
5

FILES, MODULES, PACKAGES

9 Hours
Files and exceptionsText filesReading and writing filesFormat operatorCommand line argumentsErrors and exceptionsHandling exceptionsModules and packagesIllustrative programs – word countCopy fileVoter's age validation

Course Outcomes

Upon completion of this course, students will be able to:

CO1

Apply computational thinking for problem solving

CO2

Write Python programs using data types and operators

CO3

Implement control flow and functions

CO4

Use compound data types effectively

CO5

Handle files and exceptions in Python

Industry Application & Relevance

How the concepts learned in this course are applied in real-world software development projects across Banking, Healthcare, and Enterprise domains over 20+ years of experience.

Professional Application

Automation scripts, data processing, DevOps tools

Textbooks & References

Textbooks

  • Allen B. Downey, 'Think Python: How to Think Like a Computer Scientist', O'Reilly
  • Karl Beecher, 'Computational Thinking: A Beginner's Guide', BCS Learning

Reference Books

  • Paul Barry, 'Head First Python', O'Reilly
  • John V. Guttag, 'Introduction to Computation and Programming Using Python', MIT Press