Course Content
Module 1: Introduction & Setup
In this module, students will get an overview of the Python programming course and understand what Python is and why it is widely used. They will learn how to install Python on their computer and set up a development environment using popular IDEs like IDLE, VS Code, or PyCharm. This module prepares students with all the necessary tools and knowledge to start writing and running Python programs effectively. Key Takeaways: Understand the purpose and scope of Python programming Set up Python on a computer or laptop Learn about different IDEs for writing Python code Gain confidence to run your first Python program
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Lesson 1.1: Course Overview and Learning Outcomes
Lesson 1.2: What is Python and Why Learn It?
Lesson 1.3: Installing Python and Setting Up Development Environment
Lesson 1.4: Introduction to IDEs (IDLE, VS Code, PyCharm)
Module 2: Python Basics
In this module, students will learn the foundational concepts of Python programming. Starting from Python syntax and indentation rules, the module covers variables, data types, operators, and basic input/output operations. By mastering these core concepts, students will be able to write simple yet functional Python programs and build a strong base for more advanced topics in programming. Key Takeaways: Understand Python syntax and the importance of indentation Learn about variables, data types, and operators Perform input and output operations in Python Gain the confidence to write simple Python programs independently
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Lesson 2.1: Python Syntax and Indentation
Lesson 2.2: Variables and Data Types
Lesson 2.3: Operators (Arithmetic, Comparison, Logical, Assignment)
Lesson 2.4: Input and Output in Python
Module 3: Control Flow
This module introduces students to Python’s control flow mechanisms, which allow programs to make decisions and execute code conditionally. Students will learn how to use conditional statements (if, else, elif) and loops (for and while) to control the execution of their programs. The module also covers special statements like break, continue, and pass to manage loop behavior. By the end of this module, students will be able to create dynamic programs that respond to different conditions and repeat tasks efficiently. Key Takeaways: Understand how to make decisions using conditional statements Learn to perform repetitive tasks with loops Control the flow of loops using break, continue, and pass Build programs that respond to different inputs and conditions
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Lesson 3.1: Conditional Statements (if, else, elif)
Lesson 3.2: Loops – for Loop
Lesson 3.3: Loops – while Loop
Lesson 3.4: Break, Continue, and Pass Statements
Module 4: Functions and Modules
This module introduces students to functions and modules, which are essential for writing reusable and organized Python code. Students will learn how to define their own functions, pass arguments, return values, and understand the scope of variables. The module also covers built-in functions, importing modules, and creating custom modules to structure code efficiently. By the end of this module, students will be able to write modular, maintainable, and scalable Python programs. Key Takeaways: Understand the concept and benefits of functions in Python Learn how to define functions and use arguments and return values Explore built-in Python functions for common tasks Understand modules and how to import/use them Write modular programs for better code organization
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Lesson 4.1: Defining Functions and Function Arguments
Lesson 4.2: Return Statement and Scope of Variables
Lesson 4.3: Lambda Functions
Lesson 4.4: Python Modules and Packages
Module 5: Data Structures
This module introduces students to Python’s core data structures, which are essential for storing, organizing, and manipulating data efficiently. Students will learn how to work with lists, tuples, sets, dictionaries, and strings. By mastering these data structures, students will be able to handle complex data, perform operations, and write programs that are both efficient and readable. Key Takeaways: Understand the purpose and types of Python data structures Work with lists, tuples, sets, and dictionaries for storing data Perform operations like adding, removing, and accessing data Manipulate and format strings effectively for output and processing
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Lesson 5.1: Lists – Creation, Indexing, Methods
Lesson 5.2: Tuples – Basics and Uses
Lesson 5.3: Sets – Basics and Operations
Lesson 5.4: Dictionaries – Key-Value Pairs, Methods
Lesson 5.5: String Manipulation and Formatting
Module 6: File Handling & Exceptions
This module introduces students to file handling and exception handling in Python, which are crucial for working with external data and building robust programs. Students will learn how to read from and write to files, manage different file modes, and handle errors gracefully using exceptions. By mastering these concepts, students can create programs that interact with files safely and recover from runtime errors efficiently. Key Takeaways: Understand how to open, read, write, and close files in Python Work with different file modes (r, w, a, rb, wb) Apply context managers (with statement) for safer file operations Understand exceptions and error handling using try, except, finally, and raise Build robust programs that handle unexpected situations gracefully
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Lesson 6.1: Reading and Writing Files
Lesson 6.2: Working with Different File Modes
Lesson 6.3: Exception Handling – try, except, finally
Lesson 6.4: Raising Exceptions and Custom Errors
Module 7: Object-Oriented Programming
This module introduces students to Object-Oriented Programming (OOP) in Python, a powerful paradigm that helps organize code using classes and objects. Students will learn core OOP concepts like encapsulation, inheritance, and polymorphism, which enable the creation of modular, reusable, and maintainable programs. Mastering OOP is essential for building real-world applications and advanced Python projects. Key Takeaways: Understand the difference between procedural and object-oriented programming Create and use classes and objects in Python Apply key OOP concepts: Encapsulation, Inheritance, Polymorphism Organize code efficiently and build reusable components Implement OOP for practical real-world scenarios
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Lesson 7.1: Introduction to OOP Concepts
Lesson 7.2: Classes and Objects
Lesson 7.3: Attributes and Methods
Lesson 7.4: Inheritance and Polymorphism
Lesson 7.5: Encapsulation and Abstraction
Module 8: Advanced Python Concepts
This module covers advanced Python topics that are essential for building efficient, scalable, and professional applications. Students will learn about decorators, generators, file and data manipulation techniques, working with external libraries, and advanced error handling. Mastering these concepts prepares students for real-world Python development and advanced projects. Key Takeaways: Understand and implement decorators and generators for efficient coding Work with advanced data structures and external libraries Apply advanced file handling and exception techniques Use Python modules and packages effectively for scalable applications Develop skills required for professional Python development and real-world projects
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Lesson 8.1: Decorators and Generators
Lesson 8.2: Advanced File and Data Handling
Lesson 8.3: Working with External Libraries (like pandas, NumPy)
Lesson 8.4: Advanced Error Handling and Logging
Lesson 8.5: Final Project – Applying All Concepts
Module 9: Advanced Topics
This module introduces students to advanced Python topics that are essential for real-world applications and professional development. Students will learn to work with built-in Python libraries, perform web scraping, interact with databases, and get an introduction to APIs. These concepts prepare students to handle more complex projects and automation tasks in Python. Key Takeaways: Use standard Python libraries like math, datetime, and random effectively Extract and process data from websites using BeautifulSoup Interact with databases using SQLite or MySQL Understand the basics of APIs for data access and integration
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Lesson 9.1: Working with Python Libraries (math, datetime, random)
Lesson 9.2: Web Scraping Basics (using BeautifulSoup)
Lesson 9.3: Working with Databases (SQLite/MySQL)
Lesson 9.4: Introduction to APIs (Optional for Advanced Learners)
Module 10: Projects
This module focuses on practical Python projects that consolidate the knowledge gained throughout the course. Students will build real-world applications, implement problem-solving techniques, and gain confidence in applying Python skills independently. Key Takeaways: Apply Python concepts in real-world scenarios Develop hands-on experience with projects involving GUI, data management, and web scraping Learn project structuring, debugging, and best practices Gain confidence in creating Python applications from scratch
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Lesson 10.1: Mini Project 1 – Calculator Application
Lesson 10.2: Mini Project 2 – To-Do List Manager
Lesson 10.3: Mini Project 3 – Web Scraping Example
Lesson 10.4: Mini Project 4 – Database Application
Module 11: Python Exercises and Practice Questions
This module contains all the practice exercises from basic to advanced Python topics in a single lesson. Students can attempt these questions to reinforce learning and prepare effectively for the final assessment.
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Practice Exercises – Python Programming
Final Test – Python Programming Assessment
This topic is designed to evaluate your understanding of the entire Python course – from basic concepts to advanced topics and projects. The final test will help you assess your skills, reinforce learning, and earn a certificate upon scoring 70% or above. Key Points: Covers all modules: Basics, Control Flow, Functions, Data Structures, File Handling, OOP, Advanced Topics, and Projects Includes multiple-choice questions, coding exercises, and scenario-based problems Auto-graded to provide instant feedback Certificate available for students achieving 70% or higher
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Final Test / Quiz
Python Programming for Beginners to Advanced

Lesson 8.3: Working with External Libraries (like pandas, NumPy)

Introduction:
Python’s external libraries like pandas and NumPy are essential for data analysis, numerical computing, and handling large datasets efficiently. This lesson covers installing, importing, and using these libraries to perform real-world tasks.

1. Installing Libraries:

  • Use pip to install external libraries.

 
pip install pandas numpy

2. NumPy – Numerical Computing:

  • NumPy provides fast, efficient operations on arrays.

Example – NumPy Array:

 
import numpy as np

# Creating an array
arr = np.array([1, 2, 3, 4, 5])
print(arr * 2) # Output: [ 2 4 6 8 10]

# Array operations
print(np.mean(arr)) # Output: 3.0
print(np.sum(arr)) # Output: 15

Advantages of NumPy:

  • Efficient memory usage

  • Vectorized operations

  • Mathematical and statistical functions

3. pandas – Data Analysis:

  • pandas provides DataFrame and Series for structured data.

Example – pandas DataFrame:

 
import pandas as pd

# Creating DataFrame
data = {"Name": ["Raman", "Amit"], "Age": [18, 20]}
df = pd.DataFrame(data)

# Display DataFrame
print(df)

# Accessing columns
print(df["Name"])

# Filtering rows
print(df[df["Age"] > 18])

Advantages of pandas:

  • Handles tabular data easily

  • Supports CSV, Excel, SQL, JSON formats

  • Powerful data manipulation functions

4. Practical Tips:

  • Use NumPy for numerical computations and pandas for data analysis

  • Learn indexing, slicing, and aggregation operations for efficiency

  • Combine pandas with CSV/JSON files for real-world applications

Learning Outcome of This Lesson:

  • Install and import external Python libraries

  • Work with NumPy arrays for numerical operations

  • Use pandas DataFrame and Series for data analysis

  • Apply these libraries to handle real-world datasets efficiently

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