Python DSA Syllabus – A to Z (Basic → Advanced) 2025

Python DSA Syllabus – A to Z (Basic → Advanced)

Section 1 – Python Fundamentals

  1. Python Installation, Environment Setup
  2. Python Syntax, Keywords, Identifiers
  3. Data Types – int, float, str, bool
  4. Type Conversion
  5. Input/Output operations
  6. Operators – Arithmetic, Assignment, Comparison, Logical, Bitwise, Membership, Identity
  7. Conditional Statements – if, if-else, elif
  8. Loops – for, while, nested loops, loop control statements (break, continue, pass)
  9. Functions – definition, arguments, return, scope, recursion
  10. Lambda functions
  11. Exception Handling – try, except, finally, raise
  12. Modules and Packages
  13. File Handling – reading, writing, appending, context manager
  14. List comprehensions, dictionary comprehensions
  15. Decorators and Generators (basic introduction)

Section 2 – Core Data Structures

  1. Lists – operations, slicing, searching, sorting
  2. Tuples – immutability, use cases
  3. Sets – operations, set theory applications
  4. Dictionaries – CRUD operations, iteration, nested dictionaries
  5. Strings – methods, formatting, pattern matching
  6. Arrays – introduction using array module or numpy
  7. Stack – implementation using lists
  8. Queue – implementation using collections.deque, queue.Queue
  9. Linked List – singly, doubly, circular linked list
  10. Hash Tables / Hash Maps – implementation and collision handling
  11. Heaps – min-heap, max-heap using heapq
  12. Trees – binary tree, binary search tree, balanced trees (AVL, Red-Black Tree overview)
  13. Graphs – adjacency list, adjacency matrix representation

Section 3 – Searching & Sorting Algorithms

  1. Linear Search
  2. Binary Search (iterative and recursive)
  3. Interpolation Search
  4. Jump Search
  5. Exponential Search
  6. Bubble Sort
  7. Selection Sort
  8. Insertion Sort
  9. Merge Sort
  10. Quick Sort
  11. Heap Sort
  12. Counting Sort
  13. Radix Sort
  14. Bucket Sort
  15. TimSort (Python’s built-in sort method)

Section 4 – Recursion & Backtracking

  1. Basics of recursion – stack frames
  2. Tail recursion
  3. Backtracking problems – N-Queens, Sudoku Solver
  4. Permutations and combinations
  5. Subsets generation
  6. Word Break Problem
  7. Maze solving algorithms

Section 5 – Advanced Data Structures

  1. Segment Tree – range queries, lazy propagation
  2. Binary Indexed Tree (Fenwick Tree)
  3. Trie – implementation, autocomplete, prefix search
  4. Disjoint Set Union (Union Find)
  5. Graph – BFS, DFS
  6. Dijkstra’s Algorithm
  7. Bellman-Ford Algorithm
  8. Floyd Warshall Algorithm
  9. Topological Sorting
  10. Minimum Spanning Tree – Prim’s, Kruskal’s
  11. Network Flow Basics (Ford-Fulkerson, Edmonds-Karp overview)

Section 6 – Dynamic Programming

  1. Introduction and problem breakdown
  2. Fibonacci Sequence (recursion vs DP)
  3. Memoization vs Tabulation
  4. Longest Common Subsequence (LCS)
  5. Longest Increasing Subsequence (LIS)
  6. 0/1 Knapsack Problem
  7. Unbounded Knapsack
  8. Coin Change Problem
  9. Matrix Chain Multiplication
  10. Partition Problems
  11. Edit Distance
  12. DP on Strings and Grids
  13. DP Optimization Techniques

Section 7 – Greedy Algorithms

  1. Greedy approach overview
  2. Activity selection problem
  3. Fractional Knapsack
  4. Huffman Encoding
  5. Job Scheduling
  6. Graph-based greedy algorithms

Section 8 – Mathematics for DSA

  1. Number theory – primes, GCD, LCM
  2. Modular arithmetic
  3. Sieve of Eratosthenes
  4. Exponentiation – iterative and recursive
  5. Probability basics
  6. Combinatorics – factorial, permutations, combinations
  7. Geometry basics – distance, angles, coordinates
  8. Fast algorithms – matrix exponentiation

Section 9 – String Algorithms

  1. Pattern Matching – naive, KMP, Rabin-Karp
  2. Z Algorithm
  3. Trie-based string operations
  4. Rolling hash
  5. Palindrome problems
  6. Suffix array and LCP (overview)
  7. Regular expressions

Section 10 – Graph Theory Problems

  1. Cycle detection
  2. Shortest path problems
  3. Strongly connected components (Kosaraju’s Algorithm)
  4. Bridges and articulation points
  5. Eulerian Path & Circuit
  6. Hamiltonian Path
  7. Graph coloring problems

Section 11 – File System, OS & Real-world Applications

  1. File parsing and manipulation
  2. Directory traversal algorithms
  3. Process scheduling (overview)
  4. Memory management simulation
  5. Cache implementation
  6. Compression algorithms
  7. Encryption basics – hash functions

Section 12 – Advanced Topics

  1. Multi-threading and concurrency basics
  2. Asynchronous programming – asyncio
  3. Networking with sockets
  4. Serialization formats – JSON, XML, Pickle
  5. Testing algorithms – unit tests, edge cases
  6. Profiling and performance optimization
  7. Space-time complexity analysis
  8. Big-O notation deep dive
  9. Interview patterns and problem-solving templates

Section 13 – Projects & Practice

  1. Implement a personal library management system
  2. Create a contact manager using Trie
  3. Build a basic recommendation system
  4. Implement real-time chat with sockets
  5. Solve at least 200 problems from platforms like LeetCode, Codeforces, GeeksforGeeks
  6. Practice problems categorized by difficulty – Easy → Medium → Hard → Expert

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