Module 1: Python Foundations

• Introduction to Programming: Why Python for Data Science, job roles, Python IDEs, environment setup.
• Basic Syntax: input/output, comments, variables, naming rules, data types, typecasting.
• Operators: arithmetic, assignment, comparison, logical, identity, membership, bitwise.
• Strings: creation, formatting, indexing, slicing, string methods.
• Lists: creation, properties, indexing/slicing, list of lists, methods, CRUD operations.
• Tuples: syntax, properties, indexing/slicing, methods.
• Sets: creation, updates, operations, differences with lists/tuples.
• Dictionaries: syntax, key:value storage, accessing elements, methods.

Module 2: Program Flow & Functions

• Conditional Statements: if, if-else, if-elif-else, setting logic.
• Loops: while, for, range, break, continue, pass, enumerate, zip, assert.
• Comprehensions: list and dictionary comprehension, syntax and use cases.
• Functions: modularity, creating/calling, arguments (positional, keyword, *args, **kwargs), return values.
• Advanced Functions: recursion, global vs local variables, higher-order functions.
• Anonymous Functions: lambda with filter, map, reduce.
• Generators: creating and using iterators.

Module 3: Modular Programming & Data Handling

• Modules: creating, importing functions/variables, Python built-in modules.
• Packages: creating packages, importing modules, NumPy, Pandas, Matplotlib basics.
• Exceptions & Error Handling: syntax/logical errors, try-except-finally.
• Files: opening, reading, writing, closing files in different modes.
• Date & Time: date, time, datetime modules, timedelta, formatting with strftime/strptime.
• Regex: using re module, search, compile, find, split, sub, meta characters.