Python Programming is a foundational course designed to introduce learners to the core principles of programming using Python. The course focuses on building logical thinking, problem-solving skills, and a strong understanding of programming constructs. It covers Python syntax, data types, control structures, functions, data structures concepts. By progressing from basic to intermediate topics, the course prepares students to develop real-world applications and serves as a stepping stone for advanced domains such as data science, artificial intelligence, automation, and software development.
| Day | Topic | Sub-Topic No. | Sub-Topic Title | Detailed Contents |
| Day 1 | Introduction to Python | 1.1 | Overview of Python | History, features, real-world applications |
| 1.2 | Python Environment Setup | Installation, IDEs, execution modes | ||
| 1.3 | Python Syntax Rules | Indentation, comments, keywords | ||
| 1.4 | Variables & Data Types | int, float, string, boolean | ||
| 1.5 | Input & Output Operations | input(), print(), formatted output | ||
| Day 2 | Control Flow & Logic | 2.1 | Operators in Python | Arithmetic, relational, logical |
| 2.2 | Conditional Statements | if, elif, else | ||
| 2.3 | Looping Constructs | for loop, while loop | ||
| 2.4 | Loop Control Statements | break, continue, pass | ||
| 2.5 | Logic Building Exercises | Decision-based programs | ||
| Day 3 | Data Structures | 3.1 | Strings | Indexing, slicing, string methods |
| 3.2 | Lists | Creation, operations, methods | ||
| 3.3 | Tuples & Sets | Immutability, set operations | ||
| 3.4 | Dictionaries | Key-value structure, methods | ||
| 3.5 | Data Traversal | Iterating collections | ||
| Day 4 | Functions & Modular Programming | 4.1 | Function Basics | Defining and calling functions |
| 4.2 | Function Arguments | Positional, keyword, default | ||
| 4.3 | Return Statements | Single and multiple returns | ||
| 4.4 | Lambda Functions | Anonymous functions | ||
| 4.5 | Modules & Packages | import, built-in modules | ||
| Day 5 | Applied Python & Practice | 5.1 | Python Libraries Overview | math, random, datetime |
| 5.2 | Introduction to NumPy | Arrays and basic operations | ||
| 5.3 | Introduction to Pandas | DataFrames, CSV handling | ||
| 5.4 | Applied Data Handling Exercise | Hands-on implementation using NumPy and Pandas on real-world datasets | ||
| 5.5 | Professional Orientation | Practical evaluation, concept revision, and guidance on career pathways | ||
| Day 6 | Hands-on Coding Practice/ Capstone Project | |||
| Know your Mentor | Contact Number | Email Id |
| Dr. Gitanjali | 8054459980 | gitanjali.e16525@cumail.in |