Course Curriculum

18 Chapters · 18 Lessons

  1. 1

    Free Preview

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      Free Preview

  2. 2

    Chapter 1: Introduction to Data Structures and Algorithms

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      Introduction to Data Structures and Algorithms

  3. 3

    Chapter 2: Arrays and Strings

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      Arrays and Strings

  4. 4

    Chapter 3: Linked Lists

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      Linked Lists

  5. 5

    Chapter 4: Stacks, Queues, and Deques

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      Stacks, Queues, and Deques

  6. 6

    Chapter 5: Hash Tables and Unordered Maps

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      Hash Tables and Unordered Maps

  7. 7

    Chapter 6: Trees and Binary Search Trees

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      Trees and Binary Search Trees

  8. 8

    Chapter 7: Heaps and Priority Queues

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      Heaps and Priority Queues

  9. 9

    Chapter 8: Graph Fundamentals

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      Graph Fundamentals

  10. 10

    Chapter 9: Graph Algorithm

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      Graph Algorithm

  11. 11

    Chapter 10: Sorting and Searching

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      Sorting and Searching

  12. 12

    Chapter 11: Greedy and Divide-and-Conquer Strategies

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      Greedy and Divide-and-Conquer Strategies

  13. 13

    Chapter 12: Dynamic Programming

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      Dynamic Programming

  14. 14

    Chapter 13: Backtracking and Recursion Patterns

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      Backtracking and Recursion Patterns

  15. 15

    Chapter 14: Advanced Data Structures: Tries, Segment Trees, and Fenwick Trees

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      Advanced Data Structures: Tries, Segment Trees, and Fenwick Trees

  16. 16

    Chapter 15: Applied DSA Patterns and Standard Template Library

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      Applied DSA Patterns and Standard Template Library

  17. 17

    Chapter 16: Best Tips and Trends for Interviews

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      Best Tips and Trends for Interviews

  18. 18

    Index

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      Index

About the Course

Kickstart Modern Data Structures and Algorithms takes you on a structured journey from the core principles of data organization to advanced problem-solving techniques used in real-world applications. The book begins with fundamental concepts, building clarity around arrays, linked lists, stacks, queues, hashing, trees, and graphs. It then progresses into essential algorithmic strategies, including sorting, searching, step-by-step methods, divide-and-conquer, dynamic programming, and backtracking. As you advance, you will explore powerful data structures such as tries, segment trees, and Fenwick trees, along with applied DSA patterns and effective use of STL for optimized implementation. The final section focuses on smart coding practices, interview preparation strategies, and emerging technology trends—ensuring learners are not only technically strong but also industry-ready. By the end of this journey, you will be well-equipped to analyze complex problems, design efficient solutions, and approach technical interviews and real-world development challenges with confidence.

About the Author

Ms. Divyashree Mallarapu is an AI/ML Engineer skilled in building production-grade AI systems across ML, Deep Learning, NLP, Computer Vision, Big Data, and Cloud, with strong Python and C++ expertise. She co-authored Kickstart Compiler Design Fundamentals and mentors students through workshops and hackathons. Mr. Sandeep Telkar R, Assistant Professor at PESITM, is a researcher and textbook author of Python for Machine Learning and other titles, with strong expertise in AI and programming. Dr. Yasmeen M. Shaikh, Professor and HoD at SGBIT, and Dr. Guruprasad Konnurmath, Assistant Professor at KLE Tech, are accomplished academicians with extensive research, publications, and contributions in AI, ML, and Computer Science.