Data Structures and Algorithms using C++

Course Description

This course provides a comprehensive understanding of fundamental and advanced data structures and algorithms using C++. It is designed to equip students with strong problem-solving skills and the ability to write efficient and optimized code. The course begins with foundational concepts such as complexity analysis and basic data structures, followed by arrays, searching, and sorting techniques. It then progresses to linked lists, stacks, queues, trees, recursion, heaps, hashing, and graph algorithms. A strong emphasis is placed on practical implementation through C++ programming. Students will develop hands-on experience by implementing real-world problems such as expression evaluation, tree traversals, shortest path algorithms, and hashing techniques. By the end of the course, students will be capable of designing efficient algorithms, analyzing their performance, and applying data structures effectively in real-world applications and competitive programming. Analyze time and space complexity of algorithms using asymptotic notations (Big-O, Theta, Omega). Implement and manipulate core data structures such as arrays, linked lists, stacks, and queues using C++. Apply sorting and searching techniques to solve computational problems efficiently. Design and implement tree-based structures including binary trees and binary search trees with traversal techniques. Use recursion and divide-and-conquer strategies such as merge sort and quick sort. Apply advanced concepts like heaps, hashing, and graph algorithms (BFS, DFS, shortest path) to solve real-world problems.

Course Fee Seats Limited

₹2500.00

Course Details

Duration
Duration
60 HRS
Duration
Course Label
SDC
Course Language
English
Duration
Course Mode
Online
Duration
Timings
7 PM - 9 PM
Days
Monday to Friday
Registration Till
25 May 2026
Duration
Tentative ClassStart Date
2nd Week of June
Duration
Eligible Schools:
1. School of Computer Science and Engineering 2. School of AI and Emerging Technologies 3. School of Computing and Artificial Intelligence
Certificate Criteria
Certificate Criteria
75% attendance, 50% score in all Exams/CA

Curriculum Snapshot

Explore the comprehensive course modules

1 Foundations Linear Data Structures

Covers basic concepts of data structures, complexity analysis, and linear structures like arrays and linked lists, focusing on memory representation and fundamental operations.

2 Searching, Sorting Problem Solving Techniques

Includes essential searching and sorting algorithms such as linear search, binary search, bubble, selection, and insertion sort, emphasizing efficiency and comparison.

3 Stack, Queue Recursion

Focuses on stack and queue data structures, their implementations and applications, along with recursion techniques and divide-and-conquer strategies like merge sort and quick sort.

4 Trees Binary Search Trees

Introduces hierarchical data structures including binary trees and BSTs, traversal techniques, and problem-solving approaches like LCA and tree-based operations.

5 Advanced Data Structures Graph Algorithms

Covers heaps, hashing techniques, and graph algorithms such as BFS, DFS, and shortest path algorithms, focusing on efficient data handling and real-world applications.

Instructor Spotlight

Learn from leading experts in stem cell research

Kumar Saurabh

Kumar Saurabh

Assistant Professor

Kumar Saurabh is an M.Tech graduate in Computer Science and Engineering from the National Institute of Technology (NIT) Rourkela and currently serves as an Assistant Professor at Lovely Professional University. He specializes in teaching Data Structures and Algorithms (C++), Design and Analysis of Algorithms, and Programming in Java, having taught over 500 students and mentored 30+ students in their academic and professional development. His core expertise includes C++, Java, Python, Data Structures & Algorithms, Object-Oriented Programming, and problem-solving. He has maintained a 300+ day coding streak on LeetCode, demonstrating strong algorithmic thinking and consistency, and has qualified GATE in 2020, 2021, and 2022. His M.Tech research focused on Machine Learning for Threat Detection in IoT Networks using Python, TensorFlow, and Google Cloud Platform. Passionate about teaching, mentoring, and continuous learning, he aims to contribute to academic excellence, research, and student success.