🧩 Modules and Contents

Module 1: Introduction to Problem Solving and Python Fundamentals (6 Hours)

This module introduces the basics of problem solving using Python. Key concepts include the Problem Analysis Chart (PAC), robust and boundary test cases, algorithms, flowcharts, and pseudocode. Students will explore Python data types (numeric, boolean, and string), regular expressions, operators, expressions, and built-in functions.

Regular Expression: Part 1: https://er-anandprem.medium.com/how-to-learn-regular-expressions-in-python-part-i-a1cfa1352131 Part 2: https://medium.com/@er-anandprem/how-to-learn-regular-expressions-in-python-part-ii-8f41a0547272

Module 2: Problem Solving Approaches and Control Flow Constructs (14 Hours)

Learners will study structured problem-solving strategies such as top-down, bottom-up, divide and conquer, and backtracking. They will implement logic using control flow mechanisms including conditional statements, branching, looping, and flow-altering statements like break, continue, and pass.

Module 3: Data Organization and Manipulation using Collections (14 Hours)

This module focuses on handling structured data using Python collections such as lists, tuples, sets, and dictionaries. Students will explore data comprehension, iteration, searching techniques (linear and binary search), and sorting algorithms (bubble, selection, insertion, quick, merge). Emphasis is placed on data modification, grouping, and organization (ordered, unordered, unique).

Module 4: Modular and Recursive Programming (14 Hours)

Students learn modular programming techniques by creating reusable functions. Topics include function definitions, return values, argument types (positional, keyword, default, and arbitrary), local/global scopes, lambda functions, decorators, and recursion. This module also includes implementation of menu-driven programs involving stacks and queues.

Module 5: Data Processing using NumPy and Pandas (10 Hours)

This module introduces the NumPy and Pandas libraries for numerical and tabular data analysis. Students will perform array operations, apply mathematical functions, handle file input/output, and conduct data manipulation tasks such as cleaning, filtering, selecting, grouping, sorting, aggregation, and merging.

Module 6: Contemporary Issues (2 Hours)

Covers emerging trends and applications in problem-solving using Python in various domains such as health, environment, logistics, and public data systems. Encourages discussions on real-world implications and innovations.

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📂 Materials & Resources

Throughout the course, I will be adding:

• Lecture materials
• Topic-wise presentations
• Jupyter notebooks and practice scripts
• Assignment problems
• Real-time classroom content

All files will be organized by module and topic, and updated regularly to reflect what’s being taught in class.

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Total Contact Hours: 60 Hours