Python Programming

Course Description:

This course offers a comprehensive introduction to Python programming, designed for beginners and intermediate learners. It equips participants with the essential programming skills needed to develop real-world applications. The course covers Python syntax, data structures, functions, file handling, and error management, and introduces modular programming techniques. Emphasis is placed on hands-on practice and problem-solving to build a strong foundation in writing clean, efficient, and readable Python code.

Whether you're aiming to enter the software industry, automate tasks, analyze data, or explore machine learning and web development, this course provides the right starting point. No prior programming experience is required—just curiosity and a willingness to learn.

Course Curriculum:

Module 1: Introduction to Python

1. Setting up Python environment (Anaconda, Jupyter, Colab)

2. Basic syntax and code structure

3. Variables and data types

4. Type casting and input/output

5. Operators and expressions

6. Conditional statements (if, elif, else)

7. Loops (for, while, nested loops)

Module 2: Data Structures in Python

1. Lists – creation, slicing, methods

2. Tuples – immutability, packing/unpacking

3. Sets – operations, uniqueness

4. Dictionaries – key-value pairs, methods

5. String operations and formatting

Module 3: Functions and Modules

1. Defining and calling functions

2. Arguments – default, keyword, variable-length

3. Return values

4. Lambda functions

5. Importing modules and libraries

6. Built-in vs. user-defined modules

Module 4: File Handling and Exception Handling

1. Reading and writing text files

2. File modes and operations

3. Using with statement

4. Exception types

5. Try-except block

6. Finally and else in exception handling

Module 5: -Oriented Programming (OOP)

1. Classes and objects

2. __init__() constructor

3. Instance vs. class variables

4. Inheritance and method overriding

5. Encapsulation and polymorphism

Module 6: Python Libraries for Data Science

1. NumPy – arrays, broadcasting, basic operations

2. Pandas – DataFrames, Series, data manipulation

3. Matplotlib – basic plotting and visualization

4. Seaborn – statistical plotting

5. Working with CSV and Excel files

Module 7: Final Project & Assessment

1. Mini-project – real-world problem using Python

2. Code walkthrough and documentation

3. Final quiz

4. Feedback and Q&A