DSA WITH PYTHON PROGRAMMING

Breadcrumb Abstract Shape
Breadcrumb Abstract Shape
Breadcrumb Abstract Shape

Data Structures and Algorithms (DSA) help in storing data efficiently and solving problems with optimized logic. Python makes DSA easier with simple syntax and built-in structures.

What Will You Learn?
  • You learn DSA with Python from Basic to Advance Labels.

Material Includes

  • Internship Certificate

Requirements

  • Laptop with good Internet connection.

Audience

  • Students and Working professionals.

Course Content

Chapter 1
📍 Chapter 1: Introduction to Data Structures & Algorithms with Python Chapter 1 introduces the core concepts of Data Structures and Algorithms (DSA) and highlights their importance in writing optimized and scalable programs. Learners will understand how choosing the right data structure can significantly improve program performance and how algorithms act as step-by-step solutions to complex problems. This chapter also includes a Python fundamentals recap to ensure every student begins with the necessary programming foundation. Even if someone is new to Python, the guided learning approach makes it easy to follow and apply concepts through practical coding exercises. Students will explore real-life scenarios where DSA plays a key role—such as social media platforms, navigation systems, gaming, and data processing—helping them relate theoretical knowledge to practical applications. 🔍 Key Topics Covered What are Data Structures and Algorithms? Why DSA matters in real-world software development How big tech companies evaluate DSA knowledge in interviews Python basics refresher: Data types, input/output, loops, functions Lists, tuples, and dictionaries introduction Problem-solving mindset and algorithmic thinking Understanding Big-O notation for performance analysis 🎯 Learning Outcomes After completing this chapter, students will be able to: ✔ Explain the role of DSA in optimizing solutions ✔ Write beginner-level Python programs confidently ✔ Compare different approaches using Time & Space complexity ✔ Build logical thinking required for advanced algorithms ✔ Get prepared for coding challenges and future chapters 🧑‍💻 Practical Activities Hands-on Python coding exercises Analyzing simple algorithm performance Quick quizzes to reinforce understanding Mini real-world problem examples

  • Chapter 1
    05:23
  • DSA with Python programming Quiz on Chapter 1

Chapter 2
📍 Chapter 2: Basic Data Structures in Python Chapter 2 dives into the foundational data structures that form the building blocks of efficient programming. Learners will explore how data is stored, accessed, and manipulated using Python’s built-in structures. The chapter focuses on understanding the characteristics, advantages, and limitations of each structure, and how to choose the right one based on the problem requirements. Through hands-on examples and real-world applications, students will learn how these structures impact performance and how they are used in software development, competitive programming, and technical interviews. 🔍 Key Topics Covered 1.Introduction to Linear Data Structures 2.Python Lists: Indexing, slicing, and list methods Dynamic behavior and memory allocation 3.Tuples: Immutability and use cases 4.Sets: Unordered storage and uniqueness of elements Set operations for fast lookups 5.Dictionaries: Key-value pairs and hashing concept Efficient data retrieval with dict methods 6.Internal representation of Python data structures 7.Time complexity comparison for operations 🎯 Learning Outcomes By the end of this chapter, students will be able to: ✔ Understand and implement Python’s core built-in data structures ✔ Select appropriate data structures for different scenarios ✔ Perform efficient insertions, deletions, and searches ✔ Analyze the performance of operations using Big-O notation ✔ Apply these data structures in problem-solving exercises 🧑‍💻 Practical Activities 1.Python coding exercises with real-life examples 2.Hands-on performance testing of list vs dictionary operations 3.Short quizzes to strengthen concepts 4.Mini-challenges focusing on selection of correct data structure

Chapter 3
📍 Chapter 3: Stacks and Queues Chapter 3 introduces Stacks and Queues — two essential linear data structures widely used in algorithm design and real-time applications. These structures help manage data in a specific and organized manner based on access restrictions, such as LIFO (Last-In, First-Out) and FIFO (First-In, First-Out). Students will learn how to implement stacks and queues using Python's built-in data structures like lists and collections.deque, as well as create custom implementations using classes. The chapter highlights practical use cases including function calls, expression evaluation, operating system scheduling, and data buffering. This chapter strengthens logical reasoning and helps learners apply these structures to solve structured and time-based problems effectively. 🔍 Key Topics Covered 1.Concept of Stack and its operations: push, pop, peek, isEmpty 2.Practical applications of Stacks: Undo/Redo functionality Balanced parentheses checking Expression evaluation (Prefix, Postfix, Infix) 3.Concept of Queue and its operations 4.Types of Queues: Simple Queue Circular Queue Priority Queue Double-ended Queue (Deque) 5.Implementing Stacks and Queues in Python 6.Time and Space Complexity of operations 🎯 Learning Outcomes By completing this chapter, students will be able to: ✔ Explain LIFO and FIFO principles ✔ Implement stacks and queues manually and using Python modules ✔ Apply these structures in real-world logical problems ✔ Analyze performance efficiency of stack and queue operations ✔ Strengthen algorithmic thinking through hands-on applications 🧑‍💻 Practical Activities 1.Stack simulation exercises 2.Parentheses balancing and expression conversion tasks 3.Queue scheduling problem practice 4.Short quizzes and coding challenges

Chapter 4
📍 Chapter 4: Linked Lists Chapter 4 focuses on Linked Lists, one of the most fundamental dynamic data structures in computer science. Unlike arrays, linked lists allow efficient memory usage by storing data elements in separate nodes connected through pointers. This chapter helps learners understand how data is stored dynamically and how nodes are linked to form various types of linked structures. Students will explore how to create, traverse, insert, and delete nodes in a linked list using Python. The chapter also covers different variants of linked lists and explains where they are used in real-world scenarios such as memory management, navigation systems, and implementation of other data structures like stacks and queues. This chapter encourages logical thinking and teaches how to break down operations step-by-step to handle pointer-based programming efficiently. 🔍 Key Topics Covered 1.Introduction to nodes and pointers 2.Structure of a singly linked list 3.Types of linked lists: Singly Linked List Doubly Linked List Circular Linked List 4.Operations on linked lists: Insertion (beginning, end, specific position) Deletion of nodes Searching through nodes Traversal techniques 5.Linked list implementation using Python classes 6.Comparison with arrays in terms of performance and memory 7.Time & space complexity analysis of linked list operations 🎯 Learning Outcomes By completing this chapter, students will be able to: ✔ Understand dynamic memory allocation and pointer concepts ✔ Implement different types of linked lists in Python ✔ Perform major operations on linked structures efficiently ✔ Compare linked lists with arrays based on use cases ✔ Solve real-world problems using linked list logic 🧑‍💻 Practical Activities 1.Hands-on implementation of singly and doubly linked lists 2.Node manipulation coding exercises 3.Debugging pointer-based logic 4.Mini tasks based on insertion and deletion operations 5.Interview-style linked list problems

Chapter 5
📍 Chapter 5: Searching and Sorting Algorithms Chapter 5 focuses on two critical pillars of Data Structures and Algorithms — Searching and Sorting. These algorithms help retrieve and organize data efficiently, making them essential for real-world problem-solving, system optimization, and technical interview preparation. In this chapter, learners will understand the differences between linear vs. optimized search approaches and explore how sorting algorithms impact performance in applications like databases, file systems, and competitive coding. Each algorithm is broken down with step-by-step explanations, Python implementations, and complexity analysis. 🔍 Key Topics Covered 1.What are Searching and Sorting Algorithms? 2.Searching Techniques: Linear Search Binary Search Iterative & Recursive implementations Requirements for Binary Search 3.Sorting Algorithms: Bubble Sort Selection Sort Insertion Sort Merge Sort Quick Sort Comparison of Sorting Techniques 4.Stability, Adaptability, and In-place sorting concepts 5.Time & Space Complexity of all algorithms using Big-O notation 🎯 Learning Outcomes After completing this chapter, learners will be able to: ✔ Implement major searching and sorting algorithms in Python ✔ Select the best technique based on input size and conditions ✔ Analyze algorithm performance for optimal decision-making ✔ Understand divide-and-conquer strategy through Merge & Quick Sort ✔ Solve coding interview problems on searching and sorting 🧑‍💻 Practical Activities 1.Hands-on coding tasks for every algorithm 2.Performance comparison through test cases 3.Searching and sorting real datasets 4.Beginner to intermediate-level interview challenges

Chapter 6
📍 Chapter 6: Trees, Graphs & Advanced Algorithms Chapter 6 concludes the course with some of the most powerful and widely used data structures in computer science — Trees and Graphs. These structures help solve complex real-world problems such as network routing, hierarchical data management, AI search solutions, and database indexing. Learners will also explore advanced algorithmic techniques that form the backbone of modern applications. This chapter provides a strong conceptual and practical understanding of how trees and graphs are represented, traversed, and utilized efficiently in Python. Additionally, students will learn key algorithm paradigms including Greedy Algorithms and Dynamic Programming, often seen in coding interviews and competitive programming. 🔍 Key Topics Covered 1.Trees Structure & terminology (nodes, edges, height, degree) Binary Trees & Binary Search Trees (BST) Tree Traversals: Inorder, Preorder, Postorder Practical uses of BST in search operations 2.Graph Data Structure Graph representation (Adjacency List & Matrix) Directed vs Undirected Graphs Traversal algorithms: BFS, DFS Real-world graph applications (maps, social networks) 3.Advanced Algorithms Greedy strategy and common problems Introduction to Dynamic Programming (memoization & tabulation) Understanding optimization trade-offs 4.Time & Space complexity implications in advanced structures 🎯 Learning Outcomes By completing this final chapter, learners will be able to: ✔ Implement tree and graph structures in Python ✔ Solve real-world problems using BFS, DFS, and traversal techniques ✔ Apply greedy and dynamic programming concepts to optimize solutions ✔ Analyze complex problems and choose the best algorithm approach ✔ Become confident and interview-ready in advanced DSA topics 🧑‍💻 Practical Activities 1.Python implementation exercises on BST, BFS & DFS 2.Real-world graph problem-solving tasks 3.Greedy & DP coding challenges 4.Final practice quiz and mini-project

Tags

A course by

Shankar kumar
Shankar kumar
AI Engineer

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Course Includes:

  • Price:
    Original price was: ₹900.00.Current price is: ₹400.00.
  • Instructor:Shankar kumar
  • Duration: 20 hours
  • Lessons:6
  • Students:1
  • Level:Intermediate
₹400.00 ₹900.00

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