DSA Level 2 – Advanced

Master heap data structures and their applications in scheduling, priority systems, and graph algorithms.

Practice Problems:

• Kth Largest/Smallest Element using Heap
• Merge K Sorted Arrays
• Implement Priority Queue
• Convert array to Max Heap

• Binary Heap (min/max) creation and operations
• Heapify, Insert, Delete
• Priority Queue implementation
• Heap Sort algorithm

Mini Project:

Task Scheduler – Implement a task scheduler that assigns jobs based on priority using a min-heap.

Learn efficient data retrieval using hashing and explore collision-handling methods for real-world use cases.

Practice Problems:

• Two Sum using HashMap
• Count frequencies of elements
• Find duplicates in an array
• Longest Consecutive Subsequence

• Hash functions and hashing techniques
• Collision resolution (Chaining, Open Addressing)
• HashMap & HashSet implementation concepts
• Frequency counting and lookups

Mini Project:

Student Attendance Tracker – Use hashing to store and search student attendance data efficiently.

Understand graphs as a universal structure for networks and relationships. Learn different representations and traversals.

Practice Problems:

• Graph Traversal (BFS, DFS)
• Detect Cycle in Undirected Graph
• Count Connected Components
• Check Bipartite Graph

• Graph representations (Adjacency List & Matrix)
• BFS and DFS Traversals
• Connected components
• Cycle detection in graphs

Mini Project:

Friend Network Explorer – Model a social network and use DFS/BFS to find mutual connections or friend suggestions.

Understand the power of overlapping subproblems and optimal substructure. Learn how to solve recursive problems efficiently with DP..

Practice Problems:

• Fibonacci (DP version)
• 0/1 Knapsack Problem
• Subset Sum
• Minimum Coin Change

• Recursion to DP conversion
• Memoization and Tabulation
• Classic 1D and 2D DP patterns
• Subset and knapsack patterns

Mini Project:

Resource Allocator – Optimize resource usage based on constraints using knapsack-based dynamic programming.

Explore advanced DP problems like sequence alignment, matrix path, and string transformation, crucial for interviews.

Practice Problems:

• Longest Common Subsequence
• Edit Distance
• Unique Paths in Grid
• Palindrome Partitioning

• Longest Increasing Subsequence (LIS)
• Longest Common Subsequence (LCS)
• Matrix Path and Grid-based DP
• Edit Distance and Palindrome Partitioning

Mini Project:

Text Similarity Analyzer – Use DP (Edit Distance) to measure similarity between two text strings.

Learn when and how greedy algorithms provide optimal solutions. Explore famous problems that use local optimization principles.

Practice Problems:

• Job Sequencing Problem
• Minimum Platforms
• Huffman Encoding Tree
• Fractional Knapsack

• Greedy strategy formulation
• Activity selection and interval scheduling
• Huffman Coding
• Fractional Knapsack

Mini Project:

File Compression Simulator – Implement Huffman coding to compress text data efficiently.

Reinforce problem-solving through divide-and-conquer paradigms used in sorting, searching, and matrix operations.

Practice Problems:

• Find Median of Two Sorted Arrays
• Search in Rotated Sorted Array
• Count Inversions
• Closest Pair of Points

• Master Theorem & Recurrence Relations
• Merge Sort, Quick Sort revisited
• Binary Search on Answer
• Matrix Multiplication (Strassen’s Algorithm)

Mini Project:

Performance Analyzer – Compare divide-and-conquer algorithms (Merge, Quick, Strassen) for time complexity on different datasets.