Algorithms & Problem Solving Workflow

A structured roadmap to master algorithmic thinking, data structures, and problem-solving techniques with timeline and progress tracking

12
Weeks
60
Lessons
200+
Problems

Algorithmic Thinking Roadmap

1

Algorithm Fundamentals Beginner

Week 1-2

Understand what algorithms are, algorithm analysis (Big O notation), time and space complexity, and basic algorithmic patterns.

Complexity Analysis Basic Patterns Performance Metrics
2

Sorting Algorithms Beginner

Week 3

Master fundamental sorting algorithms: Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, and Heap Sort with analysis of each.

Sorting Visualizations Implementation Comparisons
3

Searching Algorithms Beginner

Week 4

Learn linear search, binary search, depth-first search (DFS), breadth-first search (BFS), and their applications in problem-solving.

Tree/Graph Traversal Binary Search Problems
4

Array & String Manipulation Intermediate

Week 5

Master array and string algorithms: two-pointer technique, sliding window, prefix sum, and common string manipulation patterns.

Sliding Window Two Pointers Kadane's Algorithm
5

Linked Lists Intermediate

Week 6

Master singly and doubly linked lists operations, cycle detection, reversal, merging, and common interview problems.

Cycle Detection Reversal Techniques
6

Trees & Binary Search Trees Intermediate

Week 7

Learn tree traversals (in-order, pre-order, post-order), BST operations, balancing trees (AVL, Red-Black), and tree-based problems.

Tree Traversals Balanced Trees BST Operations
7

Graphs & Graph Algorithms Intermediate

Week 8

Master graph representations, DFS, BFS, Dijkstra's algorithm, topological sort, and union-find (disjoint set) data structure.

Graph Representations Shortest Path Topological Sort
8

Dynamic Programming Advanced

Week 9-10

Learn DP fundamentals: memoization, tabulation, state transition, and solve classic problems like knapsack, LCS, and edit distance.

Knapsack Problems State Transitions Memoization Patterns
9

Greedy Algorithms Intermediate

Week 11

Understand greedy strategy, proof techniques, and solve problems like activity selection, Huffman coding, and minimum spanning trees.

Proof Techniques Activity Selection MST Algorithms
10

Backtracking Advanced

Week 12

Master backtracking techniques for constraint satisfaction problems: N-Queens, Sudoku solver, permutation generation, and subset problems.

N-Queens Sudoku Solver Constraint Satisfaction
11

Hash Tables & Sets Intermediate

Week 13

Master hash table implementations, collision resolution, load factor, and applications in problem-solving for optimal time complexity.

O(1) Operations Collision Handling
12

Heaps & Priority Queues Intermediate

Week 14

Learn binary heap implementation, heap operations, heap sort, and applications like finding k-th largest elements and Dijkstra's algorithm.

Heap Operations Heap Sort Top-K Elements
13

Problem-Solving Strategies Expert

Week 15

Learn systematic approaches to problem-solving: problem decomposition, pattern recognition, simplification, and testing strategies.

Pattern Recognition Problem Decomposition Testing Strategies
14

Mock Interviews & Assessment Expert

Week 16

Participate in mock technical interviews, solve real interview problems, complete final assessment, and earn certification.

Mock Interviews Final Assessment Interview Prep

Algorithm Learning Tips

Solve Problems Daily

Consistency is key. Solve at least 1-2 algorithm problems daily to build pattern recognition and problem-solving skills.

Time Your Solutions

Practice solving problems under time constraints to simulate real interview conditions and improve efficiency.

Visualize Algorithms

Draw diagrams and visualize data structures and algorithm flows to better understand how they work.

Review & Refactor

Regularly review previously solved problems and refactor solutions to find more optimal approaches.