Training Type

Select faculty

Select Date

Dur:
Course fee : /-

Data Structures and Algorithms with Python

Course Overview

The Data Structures and Algorithms with Python course is designed to help students master the backbone of programming efficient data handling and logical problem-solving.

This course provides a complete understanding of how data is organized, stored, and processed using Python. You’ll explore core concepts like arrays, linked lists, stacks, queues, trees, sorting, and searching algorithms in a simple, intuitive way. By combining Python’s simplicity with solid algorithmic thinking, this course enables learners to write cleaner, faster, and more optimized code. Whether you’re preparing for coding interviews or aspiring to build real-world software applications, this course lays a strong technical foundation that enhances your analytical and programming skills for a successful IT career.

Description

This course focuses on developing your ability to solve computational problems using Data Structures and Algorithms (DSA) implemented in Python. It takes a hands-on approach, where each topic is supported with live examples, coding exercises, and projects that bridge theory with practice. You’ll start with the basics of Python programming, understand time and space complexities, and gradually move into advanced structures like graphs, heaps, recursion, and dynamic programming.

The course emphasizes problem-solving strategies, performance optimization, and algorithmic efficiency, which are key to cracking technical interviews and excelling in real-world development. By the end, you’ll not only master DSA concepts but also gain the confidence to apply them effectively in AI, web development, and cloud-based applications. This course is the ideal stepping stone for anyone aiming to become an efficient, job-ready Python developer.

Course Objectives

The primary objective of this course is to equip learners with a deep understanding of how algorithms and data structures shape software performance. You will learn to identify which data structure best fits a given problem, analyze algorithm efficiency, and implement practical coding solutions using Python.

The course also focuses on building problem-solving confidence through pattern-based questions and real-world scenarios. Another key goal is to prepare students for technical interviews by covering commonly asked coding challenges from top IT and product-based companies. By the end of the program, you’ll have the knowledge and experience to design efficient algorithms, optimize code, and handle complex data-driven applications skills that are crucial for developers, data analysts, and AI professionals in today’s competitive industry.

Prerequisites
  • No advanced background is required to begin this course just a willingness to learn and think logically. However, to make the most of this program, having a basic understanding of Python programming is highly recommended. Familiarity with concepts like variables, loops, conditionals, and functions will help you grasp topics faster. You should also have a fundamental awareness of how computers process data and a curiosity for solving logical problems.

    Even if you are new to programming, the course includes an introductory refresher on Python essentials, ensuring you can catch up quickly. A working laptop with Python installed and an internet connection for practicing online problems will be all you need. Most importantly, bring an analytical mindset and consistency in practice because mastering data structures and algorithms is not about memorizing, but about understanding how to approach and solve problems efficiently. This mindset will make you industry-ready for real-world development challenges.

Course Curriculum

  • Foundations of Data Structures and Algorithms (DSA)
    • What is Data Structures and Algorithms (DSA)? Explained With Examples
    • What is Algorithm? Characteristics, Types, Examples, Basics
    • Different Types of Data Structures (Classification With Examples)
    • Difference Between Linear and Non-Linear Data Structure (Comparison 2025)

  • Complexity Analysis
    • Asymptotic Notation in Data Structure (Full Guide 2025)
    • Time Complexity in Data Structure and Algorithms (Types, Examples)
    • Space Complexity in Data Structures & Algorithm (Explained With Examples)
    • Master Theorem: Formula, Example, Recurrence, Limitations

  • Fundamental Linear Data Structures
    • Arrays in Data Structure (Examples, Uses, Types, More)
    • Strings in Data Structure (Examples, Uses, Types, More)
    • Stack Data Structure: Examples, Uses, Implementation, More
    • How to Implement Stack Using Array? (C, C++, Java, Python)
    • Queue Data Structure: Types, Example, Operations, Applications, Full Guide
    • Circular Queue in Data Structure (Explained With Implementation)
    • Priority Queue in Data Structure (Explained With Implementation)
    • Linked List in Data Structures: Types, Example, Operation, Diagram
    • Linked List Operations (Insert, Delete, Traverse & 4 More)
    • Singly Linked List in Data Structure: Example, Operations, Programs
    • Doubly Linked List in Data Structure (Explained With Examples)
    • Circular Linked List in Data Structure (Types, Examples, Operations, More)
    • Circular Doubly Linked List in Data Structure (With Code Examples)

  • Advanced Non-Linear Data Structures
    • Hash Tables in Data Structure (With Implementation & Examples)
    • What is Heap Data Structure? Types, Examples, Operations, Full Guide
    • Tree Data Structure: Types, Examples, Operations, Full Guide
    • Tree Traversal in Data Structure (All Techniques With Examples)
    • Binary Tree in Data Structure (Examples, Types, Traversal, Operations)
    • Binary Search Tree (BST) in Data Structure: Full Guide
    • AVL Tree Data Structure: Rotations, Examples, Implementation
    • Red-Black Tree in Data Structure (Explained With Examples)
    • Segment Tree in Data Structure (Explained With Examples)
    • Fenwick Tree (Binary Indexed Tree): Explained With Examples
    • Full Binary Tree: Properties, Operations, Implementation
    • Perfect Binary Tree: Properties, Examples, Operations, Implementation
    • Complete Binary Tree in Data Structure (Full Guide)
    • Balanced Binary Tree (Explained With Examples & Code)
    • B Tree in Data Structure: Properties, Examples, Implementation, Full Guide
    • B+ Tree in Data Structure (Explained With Examples)
    • Graph Data Structure: Types, Uses, Examples, Algorithms
    • Graph Adjacency Matrix (Full Guide With Code Implementation)
    • Spanning Tree in Data Structure (Full Guide 2025)
    • Strongly Connected Components of Graph (Properties, Uses, Algorithms)

  • Algorithm Design
    • All Types of Sorting Algorithms in Data Structure (With Examples)
    • Bubble Sort in Data Structure (With Examples & Code)
    • Selection Sort: Algorithm, Example, Complexity, Code in C, Java, C++, Python
    • Insertion Sort: Algorithm, Example, Complexity, Code in C, C++, Java, Python
    • Merge Sort: Algorithm, Example, Complexity, Code
    • Quick Sort: Algorithm, Time & Space Complexity, Code, Example
    • Count Sort: Algorithm, Example, Complexity, Code
    • Radix Sort: Algorithm, Time Complexity, Code, Example
    • Bucket Sort: Algorithm, Time Complexity, Code, More
    • Heap Sort: Algorithm, Time & Space Complexity, Code, Example
    • Shell Sort: Algorithm, Example, Complexity, Code

  • Algorithm Design and Analysis
    • Searching Algorithms in DSA (All Types With Time Complexity)
    • Difference Between Binary Search and Linear Search Algorithm (With Examples)
    • Divide and Conquer Algorithm (Explained With Examples)
    • Recursive Algorithm: Examples, Complexity, Types, Uses
    • Greedy Algorithms: Examples, Types, Complexity
    • Backtracking Algorithm: Explained With Examples
    • All Graph Algorithms in Data Structure (With Complexity & Techniques)
    • DFS (Depth-First Search) Algorithm: Explained With Examples
    • BFS (Breadth-First Search) Algorithm
    • Bellman-Ford Algorithm: Example, Time Complexity, Code
    • Dijkstra Algorithm: Example, Time Complexity, Code
    • Kruskal Algorithm: Examples, Time Complexity, Code
    • Prim’s Algorithm: Example, Time Complexity, Code
    • Floyd-Warshall Algorithm (All-Pairs Shortest Paths)

  • Advanced Algorithmic Techniques
    • Dynamic Programming (With Problems & Key Concepts)
    • Longest Common Subsequence (LCS): Algorithm, Problems, Example, Complexity
    • Rabin-Karp Algorithm: Example, Code, Complexity, More
    • Euclidean Algorithm: GCD, Formula, Complexity, Uses
    • Ford-Fulkerson Algorithm: Example, Code, Complexity, More
    • Compression Algorithms: Types, List, Working
    • Huffman Code: Example, Algorithm, Time Complexity
    • Travelling Salesman Problem (TSP): Algorithm, Examples, Complexity

  • Bonus Topics and Performance Comparisons
    • Difference Between Stack and Queue (Easiest Comparison)
    • Array vs Linked List: All Differences With Comparison
    • Time and Space Complexity of All Sorting Algorithms
    • Time and Space Complexity of Searching Algorithms (2025)
    • Difference Between Bubble Sort and Selection Sort (2025 Comparison)
    • Difference Between Quick Sort and Merge Sort (2024 Comparison)
    • DFS vs BFS Algorithm (All Differences With Example)
Who can learn this course

The Data Structures and Algorithms with Python course is ideal for students, fresh graduates, and aspiring software developers who want to strengthen their programming and analytical abilities. It’s especially beneficial for learners preparing for campus placements, coding interviews, or competitive programming exams.

If you are pursuing a B.Tech, B.Sc, MCA, or any IT-related degree, this course will give you a strong technical edge. Working professionals aiming to transition into roles such as Python Developer, Data Analyst, Web Developer, or AI Engineer will also find this program highly valuable.

Even non-IT graduates or beginners looking to start a career in software development can benefit, as the course starts with core concepts and builds up progressively.

By the end of the training, learners will have not just theoretical knowledge but hands-on coding experience through live projects, real-time problem-solving sessions, and mentor-led practice. With Naresh i Technologies’ industry-expert trainers and practical lab environment, students will gain the confidence to tackle any technical challenge and accelerate their career in software and cloud computing domains.

Average package of course (Data Structures and Algorithms with Python)

100% Avg
salary hike
5L Avg
Package
Training Features
Comprehensive Course Curriculum

Elevate your career with essential soft skills training for effective communication, leadership, and professional success.

Experienced Industry Professionals

Learn from trainers with extensive experience in the industry, offering real-world insights.

24/7 Learning Access

Enjoy round-the-clock access to course materials and resources for flexible learning.

Comprehensive Placement Programs

Benefit from specialized programs focused on securing job opportunities post-training.

Hands-on Practice

Learn by doing with hands-on practice, mastering skills through real-world projects

Lab Facility with Expert Mentors

State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session

Our Trainees are Working with
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...

Top 5 Technologies to learn Register for the Course !

By Providing your contact details, you agree to our Terms of use & Privacy Policy