Mastering Data Structures & Algorithms

Recommended only for Advanced Learners having sound knowledge of Programming in C/C++/Java/Python.

Duration - 3 Months (80 sessions approx)

Fees - INR 12,000/-

Session Time - Evening (9:00 PM - 10:00 PM)

For more information WhatsApp on 9422317065

Salient Features

• Fully practical course

• Application oriented approach

• Best suitable for those students pursuing OR completed masters in Computer Science/ Computer Applications / IT (M.Sc. / M.C.A. ) and preparing for technical test / interview.

• Language independent approach - C, C++, Java , and Python can be used for coding. Learner can select programming language of their choice.

• Best suitable for preparation of competitive and NET/SET examinations.

Course Content

Module 1: Foundations & Complexity Analysis

Before building structures, we must learn how to measure their efficiency.

·         Introduction to DSA: Why do data structures matter? Memory management basics (Stack vs. Heap).

·         Asymptotic Notation: Big-O ($O$), Big-Omega ($\Omega$), and Big-Theta ($\Theta$).

·         Complexity Types: Best, worst, and average-case analysis.

·         Space Complexity: Measuring auxiliary memory usage.

·         Practical Math: Logarithms, summations, and recurrence relations.

Module 2: Linear Data Structures

The building blocks of data organization, where elements are stored sequentially.

1. Arrays & Dynamic Arrays

·         Static arrays vs. Dynamic arrays (e.g., Vector in C++, ArrayList in Java).

·         Amortized time complexity analysis.

·         Common operations: Insertion, deletion, access, and searching (Linear vs. Binary Search).

2. Linked Lists

·         Singly Linked Lists, Doubly Linked Lists, and Circular Linked Lists.

·         Pointer/reference manipulation and memory allocation.

·         Core Problems: Reversing a list, cycle detection (Floyd’s Tortoise and Hare), and merging sorted lists.

3. Stacks & Queues

·         Stacks: LIFO (Last In, First Out) principle. Implementation using arrays and linked lists.

o    Applications: Expression evaluation (Infix to Postfix), undo/redo functionality, and recursion simulation.

·         Queues: FIFO (First In, First Out) principle. Standard, Circular, and Deque (Double-ended queue).

o    Applications: CPU scheduling, buffering.

·         Priority Queues: Introduction to priority-based processing.

Module 3: Associative & Hash-Based Structures

Optimizing lookup, insertion, and deletion times to near-instantaneous speed.

·         Hashing Concept: Hash functions, direct address tables.

·         Collision Resolution Techniques:

o    Chaining: Linked list bucketing.

o    Open Addressing: Linear probing, quadratic probing, and double hashing.

·         Load Factor & Rehashing: Managing performance degradation.

·         Real-world Tools: HashMap / HashSet internal mechanics.

Module 4: Hierarchical Data Structures (Trees)

Moving away from linear structures to represent hierarchical data.

1. Binary Trees & Binary Search Trees (BST)

·         Tree anatomy: Root, parent, child, leaf, height, and depth.

·         Tree Traversals: Breadth-First (Level-Order) vs. Depth-First (Pre-order, In-order, Post-order).

·         BST Operations: Search, insertion, and deletion (handling 0, 1, or 2 children).

2. Self-Balancing Trees

·         The problem of skewed trees ($O(N)$ degeneration).

·         AVL Trees: Rotations (LL, RR, LR, RL) and balance factors.

·         Red-Black Trees: Properties, coloring rules, and casual introduction to industry use (e.g., C++ std::map).

3. Heaps (Binary Heaps)

·         Max-Heap and Min-Heap properties.

·         Array representation of a complete binary tree.

·         Heapify operation, insertion, deletion, and an introduction to Heapsort.

Module 5: Advanced Nonlinear Structures (Graphs)

Modeling complex relationships and networks.

·         Graph Terminology: Directed vs. Undirected, Weighted vs. Unweighted, Cycles.

·         Graph Representations: Adjacency Matrix vs. Adjacency List (Time/Space trade-offs).

·         Graph Traversals:

o    Breadth-First Search (BFS): Shortest path in unweighted graphs.

o    Depth-First Search (DFS): Connectivity, cycle detection.

·         Shortest Path Algorithms: Dijkstra's Algorithm (greedy approach) and Bellman-Ford.

·         Minimum Spanning Trees (MST): Prim's and Kruskal's Algorithms.

Module 6: Essential Algorithmic Paradigms

Structuring code using proven algorithmic methodologies.

Paradigm	Core Concept	Classic Examples
Recursion	A function calling itself to solve smaller sub-problems.	Factorials, Fibonacci, Tower of Hanoi
Divide & Conquer	Splitting a problem, solving recursively, and combining.	Merge Sort, Quick Sort
Greedy Method	Making the locally optimal choice at each step.	Fractional Knapsack, Huffman Coding
Dynamic Programming	Storing results of sub-problems to avoid recomputation.	0/1 Knapsack, Longest Common Subsequence
Backtracking	Brute force exploration with pruning when a path fails.	N-Queens Problem, Sudoku Solver