Data Structures and Algorithms in C

Overview:
This is an intermediate to expert level course intended for students and software professionals who already have a basic working knowledge of C programming.

Who is it for?

Students pursuing Computer Science or related fields
Software professionals looking to strengthen their fundamentals
Anyone interested in writing efficient and optimized code in C

What you’ll learn?
This course will help you understand and implement essential data structures (like arrays, linked lists, stacks, queues, trees, and graphs) and key algorithms (such as searching, sorting, recursion, and dynamic programming) using the C programming language. You’ll also learn how to analyze algorithm performance and write code that is both efficient and maintainable.

By the end of this course, participants will be able to:
Core Programming Skills
Write efficient and optimized C code for solving complex computational problems.
Understand and implement recursion, iteration, and memory management in C.
Data Structures
Implement and use core data structures such as arrays, linked lists, stacks, queues, trees, heaps, hash tables, and graphs.
Choose the right data structure based on problem requirements and constraints.
Build custom data structures from scratch using C.
Algorithmic Thinking
Analyze time and space complexity using Big-O notation.
Solve problems using fundamental algorithms such as searching, sorting, and traversals.
Apply algorithm design paradigms like:
Greedy algorithms
Divide and conquer
Backtracking
Dynamic programming
Dynamic Programming Mastery
Identify problems that can be solved using dynamic programming.
Apply both top-down (memoization) and bottom-up (tabulation) approaches.
Solve classic DP problems like 0/1 Knapsack, LCS, and Matrix Chain Multiplication.
Problem-Solving & Interview Preparation
Build strong problem-solving skills through hands-on coding exercises.
Practice frequently asked technical interview questions.
Learn how to approach and solve competitive programming problems.
Project and Real-world Applications
Apply your skills to build a mini project involving multiple data structures and algorithms.
Understand the relevance of DS & Algos in real-world software development.

Course Content

Module 1: Data Structures and Algorithms

Overview of Data Structures and Algorithms
Time and Space Complexity
Recursion and Backtracking

Module 2: Linear Data Structures

Arrays
Strings
Linked Lists
Singly Linked List
Doubly Linked List
Circular Linked List
Array based Stack Implementation
Linked list based Stack Implementation
Applications: Expression Evaluation, Syntax Parsing
Queues
Circular Queue
Priority Queue
Double-Ended Queue (Deque)

Module 3: Non-Linear Data Structures

Trees
Binary Trees
Binary Search Trees
Tree Traversals
In order traversal
Pre-order Traversal
Post-order Traversal
Balanced Trees
AVL Trees
Red Black Trees
Heap Trees
Min Heap
Max Heap

Module 4: Graphs

Module 5: Dynamic Programming

Introduction to Dynamic Programming
Difference between Recursion, Memoization, and Tabulation
When to apply Dynamic Programming?
Top-down vs Bottom-up approaches
Space and Time Complexity Analysis in DP
Classic 1D DP Problems
Fibonacci Numbers (Recursion → Memoization → Tabulation)
Climbing Stairs (ways to reach the top)
Minimum Cost Climbing Stairs / Frog Jump Variants
House Robber Problem
Maximum Sum of Non-Adjacent Elements
2D DP Problems
0/1 Knapsack Problem
Subset Sum Problem
Equal Sum Partition
Count of Subsets with Given Sum
Target Sum Problem
String-based DP Problems
Longest Common Subsequence (LCS)
Longest Common Substring
Edit Distance / Levenshtein Distance
Longest Palindromic Subsequence
Minimum Insertions/Deletions to make Palindrome
Advanced DP Problems
Matrix Chain Multiplication
Palindrome Partitioning
Egg Dropping Problem
Boolean Parenthesization
Wildcard Matching / Regular Expression Matching
Optimal Binary Search Tree (OBST)
DP Optimization Techniques
State Compression (Reducing 2D → 1D)
Bitmask DP (for subset problems)
DP with Binary Search (e.g., LIS using patience sorting)
DP on Trees (Tree DP)
DP with Graphs (e.g., shortest path with constraints)

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A course by

Jeganathan

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Price:
Original price was: ₹25,000.00.Current price is: ₹12,500.00.
Instructor:Jeganathan
Lessons:71
Students:0
Level:Intermediate

What Will You Learn?

Course Content

Module 1: Data Structures and Algorithms

Overview of Data Structures and Algorithms

Time and Space Complexity

Recursion and Backtracking

Module 2: Linear Data Structures

Arrays

Strings

Linked Lists

Singly Linked List

Doubly Linked List

Circular Linked List

Array based Stack Implementation

Linked list based Stack Implementation

Applications: Expression Evaluation, Syntax Parsing

Queues

Circular Queue

Priority Queue

Double-Ended Queue (Deque)

Module 3: Non-Linear Data Structures

Trees

Binary Trees

Binary Search Trees

Tree Traversals

In order traversal

Pre-order Traversal

Post-order Traversal

Balanced Trees

AVL Trees

Red Black Trees

Heap Trees

Min Heap

Max Heap

Module 4: Graphs

Representation: Adjacency List vs Matrix

Graph Traversals

Breath First Search Algorithm

Depth First Search Algorithm

Shortest Path

Cycle Detection

Module 5: Dynamic Programming

Introduction to Dynamic Programming

Difference between Recursion, Memoization, and Tabulation

When to apply Dynamic Programming?

Top-down vs Bottom-up approaches

Space and Time Complexity Analysis in DP

Classic 1D DP Problems

Fibonacci Numbers (Recursion → Memoization → Tabulation)

Climbing Stairs (ways to reach the top)

Minimum Cost Climbing Stairs / Frog Jump Variants

House Robber Problem

Maximum Sum of Non-Adjacent Elements

2D DP Problems

0/1 Knapsack Problem

Subset Sum Problem

Equal Sum Partition

Count of Subsets with Given Sum

Target Sum Problem

String-based DP Problems

Longest Common Subsequence (LCS)

Longest Common Substring

Edit Distance / Levenshtein Distance

Longest Palindromic Subsequence

Minimum Insertions/Deletions to make Palindrome

Advanced DP Problems

Matrix Chain Multiplication

Palindrome Partitioning

Egg Dropping Problem

Boolean Parenthesization

Wildcard Matching / Regular Expression Matching

Optimal Binary Search Tree (OBST)

DP Optimization Techniques

State Compression (Reducing 2D → 1D)

Bitmask DP (for subset problems)

DP with Binary Search (e.g., LIS using patience sorting)

DP on Trees (Tree DP)

DP with Graphs (e.g., shortest path with constraints)

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