← Back to Home
Data Structures & Algorithms
From Fundamentals to Scalability
Data Structures & Algorithms
Master DSA Fundamentals for Interviews, Competitive Programming & Academic Excellence - Build unshakeable foundations in problem-solving, algorithms, and data structures
Overview Curriculum Projects FAQ
📚
What You'll Master
Core data structures, algorithm design patterns, problem-solving strategies, competitive programming techniques, and interview preparation
Start Here 📖
12-Week Curriculum
Structured learning path from basic data structures to advanced algorithms and interview preparation
12 Weeks 🚀
Practice Problems
200+ LeetCode-style problems covering arrays, trees, graphs, dynamic programming, and more
3 Projects ❓
Frequently Asked Questions
Everything you need to know about the Data Structures & Algorithms course
FAQ

What You'll Master

Build unshakeable foundations in Data Structures & Algorithms for interviews, competitive programming, and academic excellence

📚 Core Data Structures

Master arrays, linked lists, stacks, queues, trees, graphs, hash tables, and heaps with real-world applications

🔍 Algorithm Design Patterns

Learn two-pointers, sliding window, recursion, backtracking, dynamic programming, and greedy algorithms

⚡ Time & Space Complexity

Understand Big-O notation, analyze algorithm efficiency, and optimize solutions for production systems

🎯 Problem-Solving Strategies

Develop systematic approaches to break down complex problems and design efficient solutions

💻 Competitive Programming

Master techniques for coding contests, LeetCode-style problems, and technical interviews

🏆 Interview Preparation

Practice with 200+ problems covering all major DSA topics asked in FAANG and top tech company interviews

12 Weeks Intensive
200+ Practice Problems
24/7 Support Available
100% Interview-Ready

12-Week Intensive Curriculum

Structured learning path from basic data structures to advanced algorithms and interview mastery

Phase 1: Foundations (Weeks 1-3)

Week 1: Arrays, Strings & Basic Algorithms
  • Arrays and dynamic arrays - operations, time complexity
  • String manipulation and pattern matching
  • Two-pointer technique and sliding window
  • Basic sorting algorithms (Bubble, Selection, Insertion)
  • Time and space complexity analysis (Big-O notation)
📝 Assignment: Solve 15 LeetCode problems covering arrays, strings, and two-pointer techniques. Implement custom sorting algorithms and analyze their complexity.
📊 Quiz: Array operations, string algorithms, two-pointer patterns, sorting algorithms, complexity analysis
Week 2: Linked Lists, Stacks & Queues
  • Singly and doubly linked lists - implementation and operations
  • Stack (LIFO) - applications and problem-solving
  • Queue (FIFO) - variants and use cases
  • Deque and priority queues
  • Solving problems using stacks and queues
📝 Assignment: Implement linked list, stack, and queue from scratch. Solve 15 problems including valid parentheses, next greater element, and LRU cache design.
📊 Quiz: Linked list operations, stack/queue applications, implementation details, problem-solving patterns
Week 3: Trees & Binary Search Trees
  • Tree fundamentals - terminology, properties, representations
  • Binary trees and binary search trees (BST)
  • Tree traversals - inorder, preorder, postorder, level-order
  • BST operations - insert, delete, search
  • Tree problem-solving patterns
📝 Assignment: Implement BST with all operations. Solve 20 tree problems including path sum, lowest common ancestor, and tree validation.
📊 Quiz: Tree concepts, BST properties, traversal algorithms, tree problem patterns, complexity analysis

Phase 2: Advanced Data Structures (Weeks 4-6)

Week 4: Graphs & Graph Algorithms
  • Graph representations - adjacency list, adjacency matrix
  • Graph traversals - Depth-First Search (DFS) and Breadth-First Search (BFS)
  • Shortest path algorithms - Dijkstra's, Bellman-Ford
  • Minimum Spanning Tree - Kruskal's and Prim's algorithms
  • Topological sorting and cycle detection
📝 Assignment: Implement graph data structure with DFS/BFS. Solve 20 graph problems including shortest path, cycle detection, topological sort, and minimum spanning tree. Analyze time complexity for each algorithm.
📊 Quiz: Graph representations, DFS/BFS algorithms, shortest path algorithms, MST algorithms, topological sorting, complexity analysis
Week 5: Hash Tables & Heaps
  • Hash tables - collision resolution (chaining, open addressing)
  • Hash functions and load factor optimization
  • Heaps - min-heap, max-heap, heap operations
  • Priority queues and heap sort
  • Advanced applications - LRU cache, frequency counting
📝 Assignment: Implement hash table with chaining and open addressing. Build min-heap and max-heap from scratch. Solve 20 problems including LRU cache, top K elements, and frequency-based problems.
📊 Quiz: Hash table operations, collision resolution strategies, heap properties, priority queue operations, heap sort, real-world applications
Week 6: Advanced Trees & Tries
  • AVL trees and self-balancing BSTs
  • Red-Black trees (concepts and properties)
  • Tries (prefix trees) - insertion, search, deletion
  • Segment trees and Fenwick trees (Binary Indexed Trees)
  • Tree problem-solving patterns and optimizations
📝 Assignment: Implement AVL tree with rotation operations. Build a Trie for string operations. Solve 20 advanced tree problems including range queries, prefix matching, and tree transformations.
📊 Quiz: Self-balancing trees, AVL rotations, Trie operations, segment trees, tree optimization techniques, complexity analysis

Phase 3: Algorithm Patterns (Weeks 7-9)

Week 7: Recursion & Backtracking
  • Recursion fundamentals - base cases, recursive calls
  • Backtracking algorithms - subsets, permutations, combinations
  • Memoization and optimization techniques
  • Divide and conquer patterns
  • Recursive tree and graph traversals
📝 Assignment: Solve 25 recursion and backtracking problems including generating subsets, permutations, N-Queens, Sudoku solver, and combination sum. Implement memoization for optimization.
📊 Quiz: Recursion patterns, backtracking strategies, memoization techniques, divide and conquer, time/space complexity of recursive solutions
Week 8: Dynamic Programming
  • DP fundamentals - memoization vs tabulation
  • 1D DP - Fibonacci, climbing stairs, house robber
  • 2D DP - unique paths, edit distance, longest common subsequence
  • Knapsack problems - 0/1 knapsack, unbounded knapsack
  • DP on strings, arrays, and grids
📝 Assignment: Solve 30 dynamic programming problems covering 1D, 2D, and advanced DP patterns. Implement both memoization and tabulation approaches. Solve classic problems like coin change, longest increasing subsequence, and matrix chain multiplication.
📊 Quiz: DP state definition, transition equations, memoization vs tabulation, space optimization, DP problem identification
Week 9: Greedy Algorithms & Advanced Patterns
  • Greedy algorithm principles and when to use
  • Interval scheduling and activity selection
  • Minimum spanning tree (greedy approach)
  • Two-pointer technique advanced patterns
  • Sliding window optimization
📝 Assignment: Solve 25 greedy algorithm problems including interval scheduling, job sequencing, and Huffman coding. Practice advanced two-pointer and sliding window problems. Compare greedy vs DP solutions.
📊 Quiz: Greedy algorithm properties, proof techniques, interval problems, two-pointer patterns, sliding window optimization

Phase 4: Advanced Topics & Interview Preparation (Weeks 10-12)

Week 10: Advanced Algorithms
  • Union-Find (Disjoint Set Union) data structure
  • String algorithms - KMP, Rabin-Karp, Z-algorithm
  • Advanced sorting - QuickSort, MergeSort, HeapSort analysis
  • Binary search variations and applications
  • Bit manipulation and bitwise operations
📝 Assignment: Implement Union-Find with path compression. Solve string matching problems using KMP algorithm. Practice 20 binary search variations. Solve 15 bit manipulation problems.
📊 Quiz: Union-Find operations, string matching algorithms, sorting algorithm analysis, binary search patterns, bit manipulation tricks
Week 11: System Design Basics & Optimization
  • Time-space tradeoffs in algorithm design
  • Designing efficient data structures for specific use cases
  • System design basics - caching, indexing, sharding concepts
  • Algorithm optimization techniques
  • Mock interview practice - coding rounds
📝 Assignment: Design efficient solutions for real-world problems (design LRU cache, design Twitter feed). Optimize existing solutions for better time/space complexity. Complete 3 mock coding interviews with detailed feedback.
📊 Quiz: Optimization strategies, tradeoff analysis, system design basics, interview problem-solving approach, complexity optimization
Week 12: Interview Mastery & Final Practice
  • FAANG interview problem patterns and strategies
  • Problem-solving frameworks and approaches
  • Code quality and best practices
  • Mock interviews - full rounds with feedback
  • Portfolio preparation and problem categorization
📝 Assignment: Complete 5 full mock interviews covering all DSA topics. Create a problem-solving portfolio with 50+ solved problems categorized by topic. Write clean, optimized code with proper documentation.
📊 Quiz: Interview strategies, problem patterns, code quality standards, communication skills, portfolio presentation

🚀 Comprehensive Practice Problems

Master DSA through 200+ carefully curated LeetCode-style problems covering all major topics

💼 Interview-Ready Practice! These problems are carefully selected to cover all DSA topics asked in FAANG and top tech company interviews. Each problem includes detailed solutions and complexity analysis.

📚 Arrays & Strings (40 Problems)

Master the fundamentals

🎯 Topics Covered:

Two-pointer technique, sliding window, string manipulation, array rotations, subarray problems, and more.

Two Pointers Sliding Window String Algorithms Array Manipulation

✨ Problem Categories

  • Easy (15): Basic array operations, string basics, simple two-pointer
  • Medium (20): Sliding window, complex string manipulation, array transformations
  • Hard (5): Advanced sliding window, complex string algorithms

🎓 Key Problems

  • Two Sum, Three Sum, Four Sum
  • Longest Substring Without Repeating Characters
  • Container With Most Water
  • Trapping Rain Water
  • Minimum Window Substring

🌳 Trees & Graphs (50 Problems)

Master tree and graph algorithms

🎯 Topics Covered:

Binary trees, BST, tree traversals, graph algorithms (DFS/BFS), shortest paths, topological sort, and more.

DFS/BFS Tree Traversals Graph Algorithms Shortest Path

✨ Problem Categories

  • Easy (15): Basic tree traversals, simple graph problems
  • Medium (25): Tree construction, graph DFS/BFS, path problems
  • Hard (10): Advanced tree problems, complex graph algorithms

🎓 Key Problems

  • Binary Tree Level Order Traversal
  • Lowest Common Ancestor
  • Serialize and Deserialize Binary Tree
  • Course Schedule (Topological Sort)
  • Network Delay Time (Dijkstra's)

💡 Dynamic Programming (45 Problems)

Master DP patterns

🎯 Topics Covered:

1D DP, 2D DP, knapsack problems, string DP, DP on trees and graphs, and optimization techniques.

Memoization Tabulation Knapsack String DP

✨ Problem Categories

  • Easy (10): Basic DP patterns, Fibonacci variations
  • Medium (25): 2D DP, knapsack, string DP
  • Hard (10): Advanced DP, optimization problems

🎓 Key Problems

  • Climbing Stairs, House Robber
  • Coin Change, Target Sum
  • Longest Common Subsequence
  • Edit Distance
  • Burst Balloons

🔄 Recursion & Backtracking (30 Problems)

Master recursive thinking

🎯 Topics Covered:

Subsets, permutations, combinations, N-Queens, Sudoku solver, and advanced backtracking patterns.

Backtracking Recursion Subsets Permutations

✨ Problem Categories

  • Easy (8): Basic recursion, simple backtracking
  • Medium (18): Subsets, permutations, combinations
  • Hard (4): N-Queens, Sudoku, complex backtracking

🎓 Key Problems

  • Subsets, Subsets II
  • Permutations, Permutations II
  • Combination Sum series
  • N-Queens
  • Sudoku Solver

⚡ Greedy & Advanced (35 Problems)

Master optimization techniques

🎯 Topics Covered:

Greedy algorithms, bit manipulation, union-find, advanced sorting, binary search variations, and more.

Greedy Bit Manipulation Union-Find Binary Search

✨ Problem Categories

  • Easy (10): Basic greedy, simple bit manipulation
  • Medium (20): Interval scheduling, advanced greedy
  • Hard (5): Complex optimization problems

🎓 Key Problems

  • Jump Game series
  • Merge Intervals
  • Task Scheduler
  • Single Number (Bit Manipulation)
  • Search in Rotated Sorted Array

🎓 Full Support for Your Learning

📝
Solution Explanations

Detailed walkthroughs for every problem

👥
Office Hours

1-on-1 help when you're stuck

🎯
Mock Interviews

Practice with real interview scenarios

📊
Progress Tracking

Track your problem-solving journey

Frequently Asked Questions

Everything you need to know about the Data Structures & Algorithms course

Q: Do I need prior DSA knowledge?
No! We start from the basics. However, you should be comfortable with at least one programming language (Python, Java, or C++).
Q: What if I miss a live session?
All sessions are recorded. You can watch later, but we encourage live attendance for interaction and immediate doubt clarification.
Q: How many problems will I solve?
You'll solve 200+ carefully curated LeetCode-style problems covering all major DSA topics. Each problem includes detailed solutions and complexity analysis.
Q: Will this help me crack coding interviews?
Absolutely! The course is specifically designed for interview preparation. We cover all major patterns asked in FAANG and top tech company interviews, plus mock interview practice.
Q: Can I complete this while working full-time?
Yes! It requires 15-20 hours/week. Evening sessions and weekend scheduling accommodate working professionals and students.
Q: What's the difference from free YouTube tutorials?
Structured curriculum, 200+ curated problems, expert mentorship, code reviews, mock interviews, and accountability. We focus on interview patterns, not just theory.

⚠️ The Job Market Harsh Reality

India produces 1.5 million engineers yearly, but only 10% secure jobs. 83% fail to find relevant employment due to the severe mismatch between college curricula and industry needs. Traditional education focuses on theory, but employers demand practical skills in Data Structures, Algorithms, and problem-solving.

1.5M Engineers Graduated Yearly
10% Get Jobs
83% Unemployed
Source: NASSCOM India Tech Industry Report 2023

Ready to Master Data Structures & Algorithms?

Join our comprehensive course and build unshakeable foundations for coding interviews and competitive programming

💬