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Python Roadmap

This roadmap is self-designed course based on experience on teaching and professional working.

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1) Foundations

Absolute basics through core language features and beginner projects.

Basics

  1. Python Shell, Math, Logical Operations
  2. Basic Syntax; Print & Formatting
  3. Variables & Typecasting
  4. Data Types (Numbers, Strings)
  5. Lists: creation, slicing, common operations
  6. Tuples
  7. Sets (intro)
  8. Dictionaries
  9. Booleans; Truthy/Falsy
  10. Conditionals (if/elif/else)
  11. Loops (for/while); Nested loops
  12. String ↔ List commonalities
  13. Functions (basics)
  14. Exceptions (intro)

Practice & Tools

  1. Loop exercises & print patterns
  2. PyCharm shortcuts & debugging
  3. Unicode (e.g., Devanagari) (bonus)
  4. Math curriculum exercises
  5. Turtle programming

2) Object‑Oriented Programming (OOP)

  1. OOP Fundamentals
  2. Classes & Objects
  3. Methods (incl. dunder methods)
  4. Inheritance & composition

3) Data Structures & Algorithms (DSA)

Core Structures

  1. Arrays & Linked Lists
  2. Stacks & Queues
  3. Heaps / Priority Queues
  4. Hash Tables
  5. Binary Search Trees

Core Techniques

  1. Recursion
  2. Searching & Sorting (selection, insertion, merge, quick)

4) Package Management

  1. PyPI
  2. pip
  3. conda

5) Advanced Python

Language Power‑Ups

  1. Functions: arguments (*args, **kwargs, defaults), scope
  2. List Comprehensions
  3. Generators & Iterators
  4. Lambdas & Expressions
  5. Decorators
  6. Regex
  7. Datetime

Working with the System

  1. OS & File Structure
  2. File Handling (read/write, CSV/JSON)
  3. Important Standard/3rd‑party Modules
  4. Virtual Environments & Paradigms

6) Web Frameworks

  1. Django
  2. Flask
  3. FastAPI
  4. Tornado

7) Automation

  1. File Manipulation & OS tasks
  2. Web Scraping
  3. GUI Automation
  4. Network Automation
  5. Automated Email Sender
  6. Getting Data from APIs

8) Testing

  1. Unit Testing
  2. Integration Testing
  3. End‑to‑End Testing
  4. Test‑Driven Development (TDD)

9) Data Science & Machine Learning

Core Stack

  1. NumPy
  2. Pandas (Spreadsheets)
  3. Matplotlib (Graphs & Maps)
  4. Seaborn
  5. Scikit‑Learn

ML Frameworks & Data

  1. TensorFlow
  2. PyTorch
  3. SQL Databases (MySQL)
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