← Back to Track
🧩
Algorithms and Data Structures
12 chapters
01Complexity and Problem-Solving Thinking
Big-O and Trade-offs
time complexity, space complexity, amortized analysis, trade-off thinking
Beginner→
Problem Decomposition
constraints, input modeling, edge cases, invariants
Beginner→
From Brute Force to Optimization
baseline solution, optimization path, pattern selection, complexity improvement
Intermediate→
02Arrays and Strings
03Linked Lists, Stacks, and Queues
04HashMaps, Sets, and Counting Patterns
05Two Pointers, Sliding Window, and Intervals
06Binary Search, Sorting, and Selection
07Trees, Heaps, and Tries
08Graphs, BFS, DFS, and Union-Find
Graph Representation
adjacency list, adjacency matrix, directed graphs, weighted graphs
Intermediate→
BFS and DFS Patterns
connected components, shortest path in unweighted graphs, flood fill, back edge detection
Advanced→
Union-Find and Topological Sort
disjoint set union, path compression, cycle detection, DAG ordering
Advanced→
09Recursion, Backtracking, and Divide and Conquer
10Dynamic Programming and Greedy
11Bit Manipulation, Math, and String Algorithms
12LeetCode Patterns and Interview Strategy
Pattern Recognition
problem signals, pattern mapping, input clues, constraint-based selection
Intermediate→
Explaining Solutions Clearly
communicating trade-offs, stating invariants, complexity explanation, interview narration
Intermediate→
Common Mistakes and Recovery
off-by-one, missed edge cases, incorrect state updates, self-correction strategy
Advanced→