Data Structures & Algorithms

Master core data structures and the algorithms that power efficient, scalable software

Build a rock-solid foundation in computer science fundamentals. Learn Big-O complexity analysis, core data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs), and the algorithms that operate on them: sorting, searching, greedy strategies, dynamic programming, backtracking, and graph traversal. Sharpen the problem-solving skills that matter for technical interviews and for writing efficient production code.

What You'll Master

📈
Big-O & Complexity Analysis

Reason about time and space efficiency, and compare algorithms objectively

🧱
Core Linear Structures

Master arrays, linked lists, stacks, and queues from first principles

🌳
Trees & Graphs

Traverse and manipulate hierarchical and networked data structures

🔑
Hashing

Design hash tables for near-constant-time lookups and inserts

🔀
Sorting & Searching

Implement and compare classic sorting and searching algorithms

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Algorithmic Problem-Solving

Apply greedy strategies, dynamic programming, and backtracking to real problems

Why Learn Data Structures & Algorithms?

Data structures and algorithms are the foundation every other engineering skill builds on. Choosing the right structure and the right approach is the difference between code that scales and code that falls over under real load. This course teaches you to recognize which structure fits a problem, analyze the efficiency of your solutions, and apply proven algorithmic strategies with confidence. These are also the exact skills tested in technical interviews at nearly every software company, making this course essential both for writing better production code and for landing your next role.

Start Learning: Big-O Notation
Build the foundation every efficient algorithm relies on!

Course Index

  1. Big-O Notation & Algorithmic Complexity
    Time and space complexity, growth rates, and how to reason about efficiency
  2. Arrays & Dynamic Arrays
    Contiguous memory, indexing, resizing strategies, and amortized cost
  3. Linked Lists (Singly & Doubly)
    Node-based structures, pointer manipulation, and common traversal patterns
  4. Stacks and Queues
    LIFO/FIFO structures, array vs linked implementations, and real-world uses
  5. Recursion Fundamentals
    Base cases, call stacks, tail recursion, and thinking recursively
  6. Hash Tables and Hashing
    Hash functions, collision resolution, load factor, and average-case lookups
  7. Trees & Binary Trees
    Tree terminology, traversal orders, and recursive tree algorithms
  8. Binary Search Trees
    Ordered trees, insertion/deletion, and balancing intuition
  9. Heaps & Priority Queues
    Min/max heaps, heapify, and priority-based scheduling problems
  10. Self-Balancing Trees
    Red-Black trees and splay trees: how automatic rotations keep a BST's height bounded
  11. Multi-Way & Disk-Oriented Trees
    B-Trees and B+ Trees: multi-key nodes designed for disk and database access patterns
  12. Tries & Range Query Trees
    Prefix trees for strings, plus segment trees and Fenwick trees for fast range queries
  13. Graphs: Representation & Traversal
    Adjacency lists/matrices, breadth-first and depth-first search
  14. Sorting Algorithms I
    Bubble, insertion, selection, and merge sort
  15. Sorting Algorithms II
    Quick sort, heap sort, and counting/radix sort
  16. Searching Algorithms & Binary Search Variants
    Linear vs binary search, and searching in rotated or sorted structures
  17. Greedy Algorithms
    Local optimization strategies and when greedy choices yield global optima
  18. Dynamic Programming Fundamentals
    Overlapping subproblems, memoization, and tabulation
  19. Dynamic Programming: Advanced Patterns
    Knapsack, longest common subsequence, and multi-dimensional DP
  20. Graph Algorithms: Shortest Paths & MST
    Dijkstra, Bellman-Ford, Kruskal, and Prim
  21. Backtracking Algorithms
    Exhaustive search with pruning: permutations, subsets, and constraint problems
  22. Capstone Project: Route Planner
    Build a route planner that composes graphs, heaps, and union-find into one product

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