1. Introduction to Python

• History of Python and its applications
• Setting up Python environment (installing Python and IDEs like PyCharm, VS Code, or Jupyter)
• Writing your first Python program
• Understanding Python syntax, comments, and indentation

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2. Basic Python Concepts

• Variables and data types
• Input and output functions
• Typecasting
• Operators (arithmetic, logical, comparison, etc.)

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3. Control Flow

• Conditional statements (if, else, elif)
• Loops (for, while)
• Break, continue, and pass statements

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4. Functions

• Defining and calling functions
• Parameters and arguments
• Return statements
• Lambda functions
• Scope and recursion

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5. Data Structures in Python

• Lists: Creating, slicing, and methods
• Tuples: Immutability and operations
• Dictionaries: Key-value pairs, methods
• Sets: Unique elements, operations
• List comprehensions and dictionary comprehensions

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6. File Handling

• Reading and writing files
• Working with text and binary files
• Handling file exceptions

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7. Object-Oriented Programming (OOP)

• Classes and objects
• Constructors (__init__ method)
• Inheritance, polymorphism, encapsulation, and abstraction
• Magic methods and operator overloading

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8. Modules and Packages

• Importing built-in modules (e.g., math, os, random)
• Creating and using custom modules
• Working with packages like pip

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9. Exception Handling

• Understanding errors and exceptions
• try, except, finally blocks
• Raising exceptions
• Custom exceptions

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10. Advanced Python Concepts

• Iterators and generators
• Decorators
• Context managers (with statement)
• Working with regular expressions (re module)

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11. Working with Libraries

• NumPy for numerical operations
• Pandas for data manipulation
• Matplotlib and Seaborn for data visualization
• Requests for handling APIs
• Flask/Django for web development basics (optional)

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12. Database Connectivity

• Connecting Python with databases like MySQL or SQLite
• Performing CRUD operations
• Using ORM tools like SQL Alchemy

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13. Python for Data Analysis and Machine Learning 

• Introduction to machine learning libraries 
• Working with datasets
• Basics of supervised and unsupervised learning

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14. Automation with Python

• Web scraping using Beautiful Soup or Selenium
• Automating tasks using os.

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15. Real-World Projects

• Building a calculator or a to-do list app
• Data analysis project with Pandas
• Web scraping project
• Flask/Django-based web app

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16. Certification Preparation 

• Review of Python certification topics
• Practice questions and interview tips

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