Mastery Program in Data Structures & Algorithms

Introduction to C/C++: Learn the basic syntax and structure of C/C++. Understand the compilation process, data types, operators, and control structures.
Memory Management: Understand pointers, dynamic memory allocation, and memory deallocation using malloc, calloc, free, and new/delete.
Functions and Recursion: Learn how to define and use functions, pass parameters, and implement recursion to solve problems.
Object-Oriented Programming (C++): Learn the principles of object-oriented programming in C++, including classes, objects, inheritance, and polymorphism.

Arrays & Strings: Learn how to use arrays for storing data and performing operations like sorting, searching, and manipulation. Work with strings to solve common problems like pattern matching.
Linked Lists: Implement singly linked lists, doubly linked lists, and circular linked lists. Learn how to perform operations like insertion, deletion, reversal, and merging.
Stacks & Queues: Implement stacks and queues using arrays and linked lists. Solve problems such as balanced parentheses and implement real-world applications like undo-redo functionality.
Hash Maps & Hashing: Learn how to implement hash tables for efficient data storage and retrieval, handle collisions, and solve problems like counting word frequencies.

Trees & Binary Trees: Learn how to implement and traverse binary trees, binary search trees, and balanced trees like AVL trees.
Heaps & Priority Queues: Understand how to implement heaps (min-heaps and max-heaps) and use them for priority-based operations like finding the smallest or largest element.
Graphs: Learn graph representations (adjacency lists, adjacency matrices) and implement graph traversal algorithms such as BFS (Breadth-First Search) and DFS (Depth-First Search).
Tries: Implement trie data structures to solve problems related to string searching, autocomplete, and spell checking.
Segment Trees & Fenwick Trees: Learn how to solve range queries efficiently using segment trees and Fenwick trees.

Sorting Algorithms: Implement and understand the time complexity of various sorting algorithms, including bubble sort, selection sort, insertion sort, merge sort, quick sort, and heap sort.
Searching Algorithms: Implement binary search, linear search, and advanced searching techniques.
Dynamic Programming: Master dynamic programming by solving common problems such as the Knapsack problem, Longest Common Subsequence (LCS), and Fibonacci sequence.
Greedy Algorithms: Learn how to apply greedy algorithms to optimization problems like the activity selection problem and Huffman coding.
Graph Algorithms: Implement and understand algorithms like Dijkstra’s shortest path, Floyd-Warshall, Kruskal’s, and Prim’s algorithms for solving shortest path and minimum spanning tree problems.

Real-World Project: Apply all the DSA concepts you’ve learned to build a complete real-world project, such as a pathfinding algorithm, recommendation system, or a data processing pipeline.
Project Presentation: Present your project to peers and instructors, demonstrating your understanding of data structures and algorithms and how they can be applied to solve real-world problems.