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The Science of Algorithms

Data Structures, Algorithms, Complexity & Computational Thinking

The Science of Algorithms
Mohammad Kamrul Hassan

Every software problem is, at its core, a problem of thinking.

How do you choose the right data structure? How do you know whether an algorithm will scale? When should you use recursion, dynamic programming, greedy methods, or graph algorithms? How can you determine whether a solution is actually correct?

The Science of Algorithms takes you from the foundations of computational thinking to the algorithmic techniques used throughout modern software.

Learn to analyze problems, design solutions, understand complexity, reason about correctness, and make informed trade-offs.

Don't just learn how algorithms work. Learn how to think like an algorithm designer.

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About

About

About the Book

The Science of Algorithms: Data Structures, Algorithms, Complexity & Computational Thinking is a practical introduction to one of the most important foundations of computer science.

Designed for CS students, programmers, aspiring developers, and early-career software engineers, the book builds knowledge progressively from fundamental computational thinking to intermediate algorithmic techniques.

It covers:

  • Computational thinking and structured problem solving
  • Algorithm design and analysis
  • Big O time and space complexity
  • Arrays and strings
  • Linked lists
  • Stacks and queues
  • Hash tables
  • Trees
  • Heaps and priority queues
  • Graphs
  • Searching algorithms
  • Sorting algorithms
  • Recursion and divide-and-conquer
  • Greedy algorithms
  • Dynamic programming
  • Backtracking
  • Fundamental graph algorithms
  • Algorithm correctness
  • Optimization and engineering trade-offs
  • Algorithms in real-world software and AI systems

Rather than focusing on a single programming language, the book emphasizes concepts that transfer across languages, platforms, and technologies.

Table of Contents

Introduction — The Science Behind Problem Solving Chapter 1 — Thinking Like a Computer Scientist Chapter 2 — Algorithms: From Problems to Solutions Chapter 3 — Measuring Algorithms: Time and Space Complexity Chapter 4 — Arrays, Strings, and Fundamental Data Organization Chapter 5 — Linked Lists: Dynamic Data Structures Chapter 6 — Stacks, Queues, and Sequential Processing Chapter 7 — Hashing and Efficient Lookup Chapter 8 — Trees and Hierarchical Data Chapter 9 — Heaps and Priority Queues Chapter 10 — Graphs: Modeling Relationships and Networks Chapter 11 — Searching Algorithms Chapter 12 — Sorting Algorithms Chapter 13 — Recursion and Divide-and-Conquer Chapter 14 — Greedy Algorithms Chapter 15 — Dynamic Programming Chapter 16 — Backtracking and Constraint Problems Chapter 17 — Fundamental Graph Algorithms Chapter 18 — Algorithm Correctness and Problem-Solving Techniques Chapter 19 — Algorithm Design, Optimization, and Trade-offs Chapter 20 — Computational Thinking in Real-World Software Conclusion Disclaimer

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AlgorithmComputers and ProgrammingSoftware EngineeringArtificial IntelligenceComputer Science

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Author

About the Author

Mohammad Kamrul Hassan

Mohammad Kamrul Hassan is an IT engineer, tech consultant, and writer specializing in global politics, information technology, and travel. A former student of Voronezh State University, Russia, he currently works as an IT engineer for a UK-based international organization and collaborates with several global technology firms. Driven by a lifelong passion for international socio-political realities and long-distance travel, his writing seamlessly blends technical depth with rich, analytical global storytelling.

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