Siksha Sarovar

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Unit 3: Python Programming

Lesson 15 of 37 in the free Data Science notes on Siksha Sarovar, written by Rohit Jangra.

Unit 3: Python Programming

Python is the most popular programming language for Data Science. Its simple syntax, vast ecosystem of libraries, and strong community support make it the go-to language for data analysis, machine learning, automation, and web development.

This unit provides a comprehensive introduction to Python programming, covering everything from basic syntax to advanced concepts like Object-Oriented Programming (OOP) and Exception Handling.

Why Python for Data Science?

FeatureBenefit
Simple SyntaxReads like English — easy to learn and write
Rich LibrariesNumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow
Large CommunityExtensive tutorials, Stack Overflow support, open-source packages
VersatilityWeb development, automation, scripting, ML, deep learning
Platform IndependentRuns on Windows, macOS, Linux without modification
Interpreted LanguageNo compilation step — write and run immediately

Key Topics Covered:

  1. Introduction to Python — History, installation, first program.
  2. Variables & Data Types — Numbers, strings, lists, tuples, dictionaries, sets.
  3. Operators — Arithmetic, comparison, logical, bitwise, assignment.
  4. Loops & Conditional Statements — if/elif/else, for, while.
  5. Functions — Defining, calling, arguments, lambda, scope.
  6. OOP in Python — Classes, objects, inheritance, polymorphism, encapsulation.
  7. File Handling — Reading, writing, and managing files.
  8. Exception Handling — try, except, finally, custom exceptions.