Syllabus
Course Overview
| Course Code | 23CSE101 |
|---|---|
| Course Title | Computational Problem Solving |
| L-T-P-C | 3-0-2-4 |
Course Objectives
To introduce computational thinking and problem-solving aspects to students through systematic treatment of algorithms, logical reasoning, and solutions. The course introduces the Python language as a tool for designing and solving these problems.
Course Outcomes
| CO | Outcome |
|---|---|
| CO1 | Apply algorithmic thinking to understand, define and solve problems. |
| CO2 | Design and implement algorithm(s) for a given problem. |
| CO3 | Apply the basic programming constructs for developing solutions and programs. |
| CO4 | Analyze an algorithm by tracing its computational states, identifying bugs and correcting them. |
CO–PO Mapping (1 = low, 2 = medium, 3 = high correlation):
| CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CO1 | 3 | 3 | 2 | 2 | 1 | — | — | — | 1 | 2 | 1 | 1 | 2 | 2 |
| CO2 | 2 | 3 | 3 | 3 | 2 | — | — | — | 1 | 2 | 1 | 1 | 3 | 3 |
| CO3 | 2 | 2 | 3 | 2 | 3 | — | — | — | 1 | 2 | 1 | 1 | 3 | 2 |
| CO4 | 2 | 3 | 3 | 3 | 2 | — | — | — | 1 | 2 | 1 | 1 | 2 | 2 |
Units
Unit 1: Problem Solving and Algorithmic Thinking Overview
- Algorithms: properties, sequence, selection, repetition
- Designing, expressing, and analyzing algorithms
- Algorithms vs. programs
- Logical reasoning and errors
- Problem definition and designing solutions
Unit 2: Overview of Programming Paradigms
- Introduction to Python: variables, strings, I/O, control flow
- Data abstraction: lists, dictionaries, tuples, sets
- Functions and recursion
- Files, debugging, and computational tracing
Unit 3: Problem Solving with Algorithms
- Searching and sorting
- Applied computational thinking: Python libraries, text processing, data analysis, chatbots, and related applications
Textbooks and References
Textbook
- Applied Computational Thinking with Python, Sofia De Jesus and Dayrene Martinez, Packt Publishing, 2020.
References
- Introduction to Computational Thinking, Thomas Mailund, Apress, 2021.
- Computational Thinking: A beginner's guide, Karl Beecher, BCS, 2017.
- The Power of Computational Thinking, Curzon P and McOwan PW, World Scientific Publishing, 2017.
Evaluation Pattern (70 : 30)
| Internal Assessment (70) | |
|---|---|
| Mid Term Examination | 20 marks |
| Continuous Assessment — Theory | 10 marks |
| Continuous Assessment — Lab | 40 marks |
| External Assessment (30) | |
| End Semester Examination | 30 marks (50-mark, 2-hour exam; theory or lab-based) |
The syllabus may change as the semester progresses.