Python DSA — Complete Guide to Data Structures & Algorithms in Python
Python Data Structures & Algorithms Tutorial
Welcome to the Python Data Structures & Algorithms tutorial track. This series takes you from solid Python fundamentals to mastery of algorithmic problem-solving — covering arrays, linked lists, trees, graphs, sorting, searching, dynamic programming, and every major algorithm design paradigm.
What is Data Structures & Algorithms?
This is a comprehensive, hands-on tutorial that teaches you data structures and algorithms using Python. You’ll learn to analyze complexity, implement data structures from scratch, apply algorithm design patterns, and solve real coding interview problems with confidence.
Data Structures & Algorithms is where you stop writing code that works and start writing code that scales efficiently. Whether you’re preparing for coding interviews at top tech companies, building high-performance applications, or competing in programming challenges — these are the skills that separate good programmers from great ones.
Tutorial Details
| Detail | Info |
|---|---|
| Level | Intermediate to Advanced |
| Chapters | 27 (with 190+ sub-topics) |
| Prerequisites | Python Basics + Python OOP completed |
| Estimated Time | 80–120 hours |
| Python Version | 3.10+ |
What You’ll Learn
- Foundations — Algorithm analysis, Big O notation, time/space complexity, recursion, memoization
- Linear Data Structures — Arrays, linked lists, stacks, queues, deques, hash tables — implemented from scratch
- Sorting & Searching — Bubble sort to merge sort to Timsort, linear to binary search and beyond
- Trees — Binary trees, BSTs, heaps, AVL trees, tries, segment trees — traversals and operations
- Graphs — Representations, BFS, DFS, Dijkstra’s, Bellman-Ford, topological sort, MST
- Algorithm Design — Two pointers, sliding window, divide and conquer, greedy algorithms, dynamic programming
- Advanced Topics — Backtracking, string algorithms, bit manipulation, bitmask DP
- Practice — Mini projects, interview problems, coding challenges, best practices, FAQ
Prerequisites
Before starting this track, you should have completed:
- Python Basics — variables, data types, functions, modules, file I/O, error handling
- Python OOP — classes, inheritance, polymorphism, design patterns, magic methods
- Comfort writing classes, using lists/dicts/sets, and working with functions and comprehensions
Not there yet? Complete the Python Basics and Python OOP tutorials first — they’re free and build the foundation you need.
Chapters
| # | Chapter | Topic Area |
|---|---|---|
| 1 | Introduction to DSA | Foundations |
| 2 | Algorithm Analysis & Big O | Foundations |
| 3 | Recursion | Foundations |
| 4 | Arrays & Dynamic Arrays | Linear Data Structures |
| 5 | Linked Lists | Linear Data Structures |
| 6 | Stacks | Linear Data Structures |
| 7 | Queues & Deques | Linear Data Structures |
| 8 | Hash Tables | Linear Data Structures |
| 9 | Basic Sorting | Sorting & Searching |
| 10 | Advanced Sorting | Sorting & Searching |
| 11 | Searching Algorithms | Sorting & Searching |
| 12 | Trees & Binary Trees | Trees |
| 13 | Binary Search Trees | Trees |
| 14 | Heaps & Priority Queues | Trees |
| 15 | Advanced Trees | Trees |
| 16 | Graph Fundamentals | Graphs |
| 17 | Graph Traversals | Graphs |
| 18 | Shortest Path & Spanning Trees | Graphs |
| 19 | Two Pointers & Sliding Window | Algorithm Design |
| 20 | Divide & Conquer | Algorithm Design |
| 21 | Greedy Algorithms | Algorithm Design |
| 22 | DP Fundamentals | Algorithm Design |
| 23 | DP Patterns | Algorithm Design |
| 24 | Backtracking | Advanced Topics |
| 25 | String Algorithms | Advanced Topics |
| 26 | Bit Manipulation | Advanced Topics |
| 27 | Practice & Next Steps | Wrap-Up |
