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Data Structures and Algorithms in Programming

A Complete Guide from Fundamentals to Advanced Techniques

This book is 100% completeLast updated on 2026-07-27
Steve Publications

Build a strong foundation in data structures and algorithms with clear explanations and practical Python examples. From core concepts to advanced techniques, this guide helps you understand how efficient software is designed so you can write better code with confidence and solve real programming challenges.

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About the Book

This book takes you from absolute beginner to professional mastery of data structures and algorithms. You will learn the theory, the implementation details, the trade-offs, and the real-world applications that matter in production software. Every concept is explained clearly with complete, working code examples in Python. No exercises or quizzes here; instead, you get thorough explanations, comparative analysis, and practical understanding that you can rely on as both a learning resource and a long-term reference.

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Data StructuresAlgorithmComputers and ProgrammingSoftware Engineering

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Steve Publications

Steve is a technology professional with more than 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. Throughout his career, he has designed, secured, analyzed and tested complex software and infrastructure, with a particular focus on understanding how systems fail and how they can be made more secure.

Outside of work, Steve enjoys sharing knowledge with the technology community. He collaborates with researchers, industry experts and technology professionals to write practical books covering software development, cybersecurity, cloud computing, networking, DevOps, artificial intelligence and enterprise technologies. His books focus on practical learning through clear explanations, real-world examples and hands-on exercises. With more than two decades of industry experience, his goal is to help IT professionals, students and technology enthusiasts build useful skills and stay current in a rapidly changing industry.

We believe readers deserve to know how our books are created. Most of our authors are not native English speakers, so we use AI to help translate, proofread manuscripts, fix grammar, improve sentence structure and make technical explanations easier to read. AI is used as an editing tool only. It does not replace the research, technical knowledge or hands-on experience behind our books. Some of our authors also prefer to remain anonymous for privacy or professional reasons. In those cases, we publish their work under a different name. The author's name may be different, but the quality of the content and our review process remain the same.

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Contents

Table of Contents

A Complete Guide from Fundamentals to Advanced Techniques

Introduction: Why Data Structures and Algorithms Matter

Chapter 1: Foundations of Programming and Algorithmic Thinking

  1. What Is an Algorithm
  2. Computational Thinking and Problem Decomposition
  3. Variables, Types, and Memory at a High Level
  4. Control Flow: Sequencing, Selection, and Iteration
  5. Functions, Modularity, and Abstraction
  6. Pseudocode vs. Real Code: Bridging the Gap
  7. Chapter 1 Summary

Chapter 2: Complexity Analysis and Big-O Notation

  1. Why Efficiency Matters
  2. Time Complexity and Running Time Models
  3. Big-O, Big-Omega, and Big-Theta Notation
  4. Analyzing Loops, Conditionals, and Function Calls
  5. Space Complexity and Memory Usage
  6. Best Case, Worst Case, and Average Case Analysis
  7. Amortized Analysis Basics
  8. Common Complexity Classes in Practice
  9. Chapter 2 Summary

Chapter 3: Recursion and Inductive Reasoning

  1. The Recursive Mindset
  2. Base Cases and Recursive Steps
  3. Tracing Recursion: Stack Frames and Call Trees
  4. Mathematical Induction as a Reasoning Tool
  5. Tail Recursion and Optimization
  6. Common Recursive Patterns
  7. When to Use (and Avoid) Recursion
  8. Chapter 3 Summary

Chapter 4: Arrays, Strings, and Linear Storage

  1. Static vs. Dynamic Arrays
  2. Memory Layout and Cache Locality
  3. Array Operations: Insertion, Deletion, Search
  4. Two-Pointer Technique and Sliding Window
  5. Strings as Specialized Arrays
  6. String Operations and Mutable vs. Immutable Designs
  7. Common Pitfalls and Off-by-One Errors
  8. Chapter 4 Summary

Chapter 5: Linked Lists, Stacks, and Queues

  1. Pointer-Based Data Structures
  2. Singly Linked Lists: Implementation and Operations
  3. Doubly Linked Lists and Bidirectional Traversal
  4. Circular Linked Lists
  5. Stacks: Array-Based vs. Linked Implementations
  6. Queues: Standard, Circular, and Priority Variants
  7. Deques and Real-World Applications
  8. Chapter 5 Summary

Chapter 6: Hashing and Hash Tables

  1. The Hashing Idea
  2. Designing Good Hash Functions
  3. Collision Resolution: Chaining vs. Open Addressing
  4. Load Factor and Dynamic Resizing
  5. Linear Probing, Quadratic Probing, Double Hashing
  6. Performance Analysis and Degenerate Cases
  7. Real-World Applications and Language Implementations
  8. Chapter 6 Summary

Chapter 7: Trees and Binary Search Trees

  1. Tree Terminology and Structure
  2. Binary Trees and Their Properties
  3. Tree Traversals: Inorder, Preorder, Postorder, Level Order
  4. Binary Search Trees: Operations and Complexity
  5. Self-Balancing Trees: AVL Trees
  6. Red-Black Trees and Their Guarantees
  7. Tree-Based Sets and Maps in Practice
  8. Chapter 7 Summary

Chapter 8: Heaps and Priority Queues

  1. The Heap Property
  2. Binary Heap Implementation as an Array
  3. Insertion, Extraction, and Heapify
  4. Build-Heap and Heap Sort
  5. Priority Queues as Abstract Data Types
  6. D-Heaps and Fibonacci Heaps Overview
  7. Applications: Scheduling, Median Finding, Top-K Problems
  8. Chapter 8 Summary

Chapter 9: Graphs and Graph Algorithms

  1. Graph Terminology and Representations
  2. Adjacency Matrices vs. Adjacency Lists
  3. Breadth-First Search and Shortest Unweighted Paths
  4. Depth-First Search and Its Applications
  5. Dijkstra’s Algorithm for Weighted Shortest Paths
  6. Bellman-Ford and Negative Edge Weights
  7. Floyd-Warshall for All-Pairs Shortest Paths
  8. Minimum Spanning Trees: Prim and Kruskal
  9. Topological Sorting and Dependency Resolution
  10. Connected Components and Union-Find
  11. Chapter 9 Summary

Chapter 10: Advanced Tree Structures — Tries, Segment Trees, and More

  1. Trie Data Structure for String Storage
  2. Trie Operations: Insert, Search, Prefix Matching
  3. Memory Optimization: Compressed Tries
  4. Segment Trees for Range Queries
  5. Building and Updating Segment Trees
  6. Lazy Propagation in Segment Trees
  7. Suffix Structures Overview
  8. Interval Trees and Geometric Applications
  9. Chapter 10 Summary

Chapter 11: Sorting Algorithms — Complete Analysis

  1. Sorting Problem Definition and Stability
  2. Comparison-Based Lower Bounds
  3. Bubble Sort, Selection Sort, Insertion Sort
  4. Merge Sort: Divide-and-Conquer in Action
  5. Quick Sort: Partitioning and Pivot Strategies
  6. Heap Sort: In-Place and Guaranteed Performance
  7. Counting Sort, Radix Sort, Bucket Sort
  8. Hybrid Sorting: Introsort and Timsort
  9. Stable Sort vs Unstable Sort: When It Matters
  10. Choosing the Right Sort for Your Use Case
  11. Chapter 11 Summary

Chapter 12: Searching Algorithms and Techniques

  1. Linear Search and Its Variants
  2. Binary Search: Implementation and Edge Cases
  3. Iterative vs. Recursive Binary Search
  4. Lower Bound, Upper Bound, and Equality Search
  5. Interpolation and Exponential Search
  6. Searching in Sorted Matrices
  7. Fractional Cascading for Multiple Searches
  8. Practical Search Library Design
  9. Chapter 12 Summary

Chapter 13: Algorithmic Paradigms — Divide-and-Conquer, Greedy, Backtracking

  1. Algorithm Design Paradigms Overview
  2. Divide-and-Conquer Strategy and Master Theorem
  3. Classic Divide-and-Conquer: Merge Sort, Quick Select, Closest Pair
  4. Greedy Algorithms and Optimal Substructure
  5. Proving Greedy Correctness with Exchange Arguments
  6. Backtracking: Systematic Search with Pruning
  7. Branch and Bound for Optimization Problems
  8. Chapter 13 Summary

Chapter 14: Dynamic Programming — From Basics to Advanced

  1. What Makes a Problem Amenable to DP
  2. Memoization vs. Tabulation
  3. Defining States and Transitions
  4. Classic 1D Problems: Fibonacci, Knapsack, LIS
  5. 2D DP: Edit Distance, Longest Common Subsequence
  6. DP on Trees and Graphs
  7. Space Optimization Techniques
  8. Advanced Patterns: Digit DP, Interval DP, Bitmask DP
  9. Chapter 14 Summary

Chapter 15: Advanced Topics — Randomized, Parallel, String, and Geometric Algorithms

  1. Randomized Algorithms and Probabilistic Analysis
  2. Quick Sort Randomization and Monte Carlo Methods
  3. Parallel Algorithms and Divide-and-Conquer Parallelism
  4. String Matching: KMP Algorithm
  5. Rabin-Karp Rolling Hash for Pattern Search
  6. Computational Geometry Basics: Convex Hull, Line Intersection
  7. Advanced Optimization: Simulated Annealing, Genetic Algorithms Overview
  8. Chapter 15 Summary

Conclusion: Putting It All Together

References

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