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Algorithms

Develop deep problem-solving skills by understanding the core algorithmic techniques that power all modern software systems — from sorting to dynamic programming.

Intermediate to AdvancedSelf-paced10 modules55 available lessons

Your path, one skill at a time.

Algorithms are the soul of programming — the logic behind every system, search, and computation. This roadmap helps you build algorithmic thinking, efficiency awareness, and the ability to deconstruct problems into elegant, optimized steps. You’ll explore major algorithmic paradigms with practical examples, real-world use cases, and performance analysis.

What you’ll learn

  • Understand time and space complexity deeply (Big O, Omega, Theta)
  • Analyze, design, and optimize algorithms for efficiency
  • Master recursion, iteration, and problem decomposition
  • Apply algorithmic paradigms like divide & conquer, greedy, and dynamic programming
  • Work with real-world graph, string, and numeric problems
  • Develop competitive programming and interview problem-solving skills

THE CURRICULUM

Build your knowledge, step by step.

55 lessons across 10 modules. Open a module to explore its lessons and practical work.

01MODULE 01Algorithm Foundations & Complexity5 lessons · Self-paced

Understanding performance, analysis, and algorithm behavior.

  1. 01What is an Algorithm?20 min
  2. 02Time & Space Complexity (Big O, Ω, Θ)20 min
  3. 03Asymptotic Analysis and Growth Rates20 min
  4. 04Common Complexity Classes (O(1), O(n), O(n log n), O(2^n))20 min
  5. 05Practice: Analyze and Compare Algorithms45 min
02MODULE 02Recursion and Iteration5 lessons · Self-paced

Breaking problems into smaller subproblems using recursion.

  1. 01Understanding Recursion and Stack Frames20 min
  2. 02Tail Recursion and Optimization20 min
  3. 03Recurrence Relations and Subproblem Trees20 min
  4. 04Iterative vs Recursive Trade-offs20 min
  5. 05Practice: Factorial, Fibonacci, Tower of Hanoi55 min
03MODULE 03Sorting Algorithms6 lessons · Self-paced

Organizing data efficiently through comparison-based and non-comparison-based methods.

  1. 01Bubble, Insertion, and Selection Sort20 min
  2. 02Merge Sort (Divide and Conquer)20 min
  3. 03Quick Sort and Partition Logic20 min
  4. 04Counting, Radix, and Bucket Sort20 min
  5. 05Sorting Stability and In-Place Concepts20 min
  6. 06Practice: Sorting Visualizations and Comparisons60 min
04MODULE 04Searching Algorithms5 lessons · Self-paced

Efficiently locating elements or patterns in structured or unstructured data.

  1. 01Linear Search and Basic Search Logic20 min
  2. 02Binary Search (Iterative & Recursive)20 min
  3. 03Ternary Search and Search Space Reduction20 min
  4. 04Applications of Binary Search (Lower/Upper Bound)20 min
  5. 05Practice: Search Problems on Arrays & Ranges50 min
05MODULE 05Divide and Conquer5 lessons · Self-paced

Breaking complex problems into independent subproblems.

  1. 01Divide and Conquer Concept20 min
  2. 02Case Studies: Merge Sort, Quick Sort, Binary Search20 min
  3. 03Matrix Multiplication and Closest Pair Problem20 min
  4. 04Master Theorem for Complexity Analysis20 min
  5. 05Practice: Recursive Tree Analysis50 min
06MODULE 06Greedy Algorithms6 lessons · Self-paced

Making optimal local choices to reach a globally optimal solution.

  1. 01Greedy Choice Property and Optimal Substructure20 min
  2. 02Activity Selection and Interval Scheduling20 min
  3. 03Huffman Encoding and Data Compression20 min
  4. 04Minimum Spanning Tree (Kruskal’s & Prim’s overview)20 min
  5. 05Greedy vs Dynamic Programming Comparison20 min
  6. 06Practice: Greedy Problem Set55 min
07MODULE 07Dynamic Programming (DP)6 lessons · Self-paced

Solving problems by storing intermediate results to avoid recomputation.

  1. 01Overlapping Subproblems and Optimal Substructure20 min
  2. 02Memoization vs Tabulation Approaches20 min
  3. 031D DP: Fibonacci, Climbing Stairs, Min Cost Path20 min
  4. 042D DP: Knapsack, Subset Sum, LCS20 min
  5. 05State Representation and Transitions20 min
  6. 06Practice: DP Challenge Set75 min
08MODULE 08Graph Algorithms6 lessons · Self-paced

Algorithms for connected, directed, and weighted graph structures.

  1. 01Graph Traversal: BFS and DFS20 min
  2. 02Shortest Path: Dijkstra, Bellman-Ford, Floyd-Warshall20 min
  3. 03Topological Sorting and DAGs20 min
  4. 04Union-Find and Connected Components20 min
  5. 05Minimum Spanning Trees (Kruskal’s and Prim’s)20 min
  6. 06Practice: Graph-Based Challenges75 min
09MODULE 09String and Pattern Algorithms6 lessons · Self-paced

Efficient text and pattern manipulation algorithms.

  1. 01String Matching Basics (Naive and Sliding Window)20 min
  2. 02KMP Algorithm (Prefix Table Concept)20 min
  3. 03Rabin-Karp and Rolling Hash20 min
  4. 04Z-Algorithm and Prefix Functions20 min
  5. 05Applications: Substring Search, DNA Matching, Logs20 min
  6. 06Practice: String Problem Set55 min
10MODULE 10Optimization and Real-World Applications5 lessons · Self-paced

How algorithms shape performance and decision-making in real systems.

  1. 01Approximation and Randomized Algorithms20 min
  2. 02Cache Optimization and Space Efficiency20 min
  3. 03Parallel Algorithms and MapReduce Basics20 min
  4. 04Algorithm Design Patterns in Industry20 min
  5. 05Capstone: Build and Analyze a Real-World Algorithm90 min