Python Automation

Module 1: Python Fundamentals

• Introduction to Python:
• History and philosophy of Python
• Why Python is popular

• Basic Syntax and Data Types:
• Variables and data types (numbers, strings, lists, tuples, dictionaries, sets)
• Operators (arithmetic, comparison, logical)
• Control flow (if-else statements, loops)

• Functions:
• Defining and calling functions
• Arguments and parameters
• Return values

• Modules and Packages:
• Importing modules and packages
• Creating your own modules

Module 2: Data Structures and Algorithms

• Lists:
• List comprehensions

• Dictionaries:
• Dictionary operations (get, keys, values, items)
• Dictionary comprehensions

• Sets:
• Set operations (union, intersection, difference, symmetric difference)

• Tuples:
• Immutable sequences

• Algorithms:
• Searching algorithms (linear search, binary search)
• Sorting algorithms (bubble sort, insertion sort, selection sort, merge sort, quick sort)

Module 3: Object-Oriented Programming (OOP)

• Classes and Objects:
• Creating classes and objects
• Attributes and methods
• Inheritance
• Polymorphism
• Encapsulation

• Special Methods:
• __init__, __str__, __repr__, etc.

Module 4: File I/O and Exception Handling

• File Operations:
• Reading and writing files
• File modes (read, write, append)

• Exception Handling:
• Try-except blocks
• Raising exceptions
• Custom exceptions

Module 5: Regular Expressions

• Introduction to Regular Expressions:
• Basic syntax and patterns
• Matching and searching text

• Common Use Cases:
• Email validation
• Data extraction
• Text processing

Module 6: Web Development with Python

• Web Development Concepts:
• HTTP requests and responses
• Routing
• Templates
• Databases (SQLAlchemy)

Module 7: Data Science and Machine Learning

• NumPy and Pandas:
• Data manipulation and analysis

• Matplotlib and Seaborn:
• Data visualization

• Machine Learning Libraries:
• Scikit-learn
• TensorFlow
• PyTorch

Module 8: Python Projects and Case Studies

• Project Selection:
• Identifying relevant problem statements
• Data acquisition and cleaning

• Project Execution:
• Data Source, Coding, Data Visualization

• Presentation and Communication:
• Effective storytelling with data

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