Python Syllabus

Course Duration: 60 Hrs

  1. Introduction to Python
    Python is an interpreted, high-level programming language known for its readability. Students will learn how to install Python and set up their development environment.
  2. Variables and Data Types
    Covers the basic types like integers, floats, strings, and booleans. Learners will understand how to assign variables and check their types using Python.
  3. Input and Output Operations
    Explains how to collect user input with input() and display information using print(). It also covers string formatting for better output display.
  4. Operators in Python
    Introduces arithmetic, logical, comparison, and bitwise operators. Students will practice using operators to build basic expressions.
  5. Conditional Statements
    Covers if, elif, and else for decision-making in programs. Real-life scenarios will demonstrate when and how to use these conditions.
  6. Loops
    Discusses the for and while loops for iteration, along with break and continue for control flow. Students will create loops to handle repetitive tasks.
  7. Functions
    Teaches how to define reusable code blocks using functions. This section covers parameters, return statements, and default arguments.
  8. Recursion
    Introduces recursive functions, where functions call themselves to solve smaller subproblems. Students will practice implementing recursion.
  9. Lists
    Covers list creation, slicing, and common methods like append, pop, and sort. Learners will understand how to manipulate list data effectively.
  10. Tuples
    Focuses on immutable sequences used for fixed data. Students will compare tuples with lists and learn tuple unpacking techniques.
  11. Dictionaries
    Introduces key-value pairs, dictionary methods, and how to loop through dictionaries. Learners will use dictionaries to store structured data.
  12. Sets
    Covers sets, which store unique elements. Students will practice using union, intersection, and other set operations.
  13. String Manipulation
    Explains string methods such as split(), join(), and slicing. Learners will work with text data and formatting techniques.
  14. Exception Handling
    Teaches how to use try, except, and finally to handle errors. Students will explore real-life scenarios to build error-free programs.
  15. File Handling
    Explores reading and writing files using open() and with statements. Learners will build small programs to process text files.
  16. Classes and Objects
    Introduces object-oriented programming by defining classes and creating objects. Students will understand how to represent real-world entities.
  17. Inheritance and Encapsulation
    Covers how classes inherit properties from other classes. Students will also learn encapsulation for data protection within classes.
  18. Polymorphism
    Demonstrates how functions and methods can behave differently based on input or objects. Real-life scenarios will show polymorphic behavior.
  19. Lambda Functions
    Teaches the use of anonymous functions for short, one-time tasks. Students will explore how lambda works with map(), filter(), and reduce().
  20. Iterators and Generators
    Explains how to create iterators for sequential data and generators for lazy evaluation. Students will practice with loops and data streams.
  21. Decorators and Context Managers
    Covers decorators to modify functions and with statements for efficient resource management. Students will create custom decorators.
  22. Regular Expressions (Regex)
    Teaches pattern matching with the re module. Students will search, extract, and manipulate strings using regex patterns.
  23. Working with Dates and Time
    Covers the datetime module for manipulating date and time. Learners will create programs that handle time-based operations.
  24. Introduction to Libraries
    Introduces libraries such as numpy for arrays, pandas for dataframes, and matplotlib for visualizations. Basic operations are demonstrated.
  25. Data Handling with Pandas
    Explores DataFrames for structured data handling. Students will perform filtering, sorting, and grouping operations on datasets.
  26. Data Visualization with Matplotlib
    Teaches how to plot data using different types of charts and graphs. Students will learn to customize plots for better readability.
  27. Web Scraping
    Introduces web scraping using requests and BeautifulSoup. Students will extract and process data from web pages.
  28. API Integration
    Covers how to send requests and process JSON responses using APIs. Learners will connect to external services via API calls.
  29. Database Connectivity
    Teaches how to connect to SQLite databases and execute SQL queries using Python. Students will create simple CRUD operations.
  30. Capstone Project
    Students will apply all the learned concepts in a final project. This will involve building a complete Python application.

 

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