Algorithms and Data Structures The Science of Computing 1st edition by Douglas Baldwin, Greg Scragg ISBN 1584502509 978-1584502500

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ISBN 10: 1584502509
ISBN 13: 978-1584502500
Author: Douglas Baldwin, Greg Scragg 

While many computer science textbooks are confined to teaching programming code and languages, Algorithms and Data Structures: The Science of Computing takes a step back to introduce and explore algorithms — the content of the code. Focusing on three core topics: design (the architecture of algorithms), theory (mathematical modeling and analysis), and the scientific method (experimental confirmation of theoretical results), the book helps students see that computer science is about problem solving, not simply the memorization and recitation of languages. Unlike many other texts, the methods of inquiry are explained in an integrated manner so students can see explicitly how they interact. Recursion and object oriented programming are emphasized as the main control structure and abstraction mechanism, respectively, in algorithm design. Designed for the CS2 course, the book includes text exercises and has laboratory exercises at the supplemental Web site.

Algorithms and Data Structures: The Science of Computing 1st  Table of contents:

1. Introduction to Algorithms and Data Structures

   • 1.1. What is an Algorithm?
   • 1.2. Importance of Data Structures
   • 1.3. Overview of the Science of Computing
   • 1.4. The Role of Algorithms in Problem Solving
   • 1.5. Classification of Algorithms and Data Structures

2. Chapter 1: Basic Data Structures

   • 2.1. Arrays and Lists
   • 2.2. Stacks and Queues
   • 2.3. Linked Lists
   • 2.4. Trees and Binary Trees
   • 2.5. Graphs and Their Representations

3. Chapter 2: Algorithm Analysis

   • 3.1. Big O Notation and Time Complexity
   • 3.2. Space Complexity
   • 3.3. Best, Worst, and Average Case Analysis
   • 3.4. Algorithm Efficiency and Optimization
   • 3.5. Practical Performance Considerations

4. Chapter 3: Sorting and Searching Algorithms

   • 4.1. Bubble Sort, Insertion Sort, and Selection Sort
   • 4.2. Merge Sort and Quick Sort
   • 4.3. Binary Search and Linear Search
   • 4.4. Hashing and Hash Tables
   • 4.5. Sorting Algorithms: Comparisons and Analysis

5. Chapter 4: Advanced Data Structures

   • 5.1. Heaps and Priority Queues
   • 5.2. AVL Trees and Red-Black Trees
   • 5.3. B-Trees and B+ Trees
   • 5.4. Trie Data Structures
   • 5.5. Disjoint Sets and Union-Find

6. Chapter 5: Graph Algorithms

   • 6.1. Representation of Graphs
   • 6.2. Depth-First Search (DFS) and Breadth-First Search (BFS)
   • 6.3. Dijkstra’s Algorithm for Shortest Path
   • 6.4. Floyd-Warshall Algorithm
   • 6.5. Minimum Spanning Tree: Kruskal’s and Prim’s Algorithms

7. Chapter 6: Dynamic Programming and Greedy Algorithms

   • 7.1. Introduction to Dynamic Programming
   • 7.2. Knapsack Problem
   • 7.3. Longest Common Subsequence
   • 7.4. Optimal Substructure and Overlapping Subproblems
   • 7.5. Greedy Algorithms and Their Applications

8. Chapter 7: Divide and Conquer Algorithms

   • 8.1. Principles of Divide and Conquer
   • 8.2. Merge Sort and Quick Sort Revisited
   • 8.3. Binary Search in Divide and Conquer
   • 8.4. Strassen’s Matrix Multiplication Algorithm
   • 8.5. Applications of Divide and Conquer

9. Chapter 8: Backtracking and Branch and Bound

   • 9.1. Principles of Backtracking
   • 9.2. Solving the N-Queens Problem
   • 9.3. Knapsack Problem with Backtracking
   • 9.4. Branch and Bound Techniques
   • 9.5. Applications and Case Studies

10. Chapter 9: Computational Complexity

    • 10.1. Introduction to NP-Completeness
    • 10.2. P vs NP Problem
    • 10.3. Approximation Algorithms
    • 10.4. NP-Hard and NP-Complete Problems
    • 10.5. Cryptographic Algorithms

11. Chapter 10: Parallel Algorithms

    • 11.1. Introduction to Parallelism
    • 11.2. Parallel Sorting and Searching
    • 11.3. MapReduce and Parallel Programming Models
    • 11.4. GPU Algorithms
    • 11.5. Efficiency and Scalability of Parallel Algorithms

12. Appendices

    • A.1. Review of Mathematical Foundations
    • A.2. Common Programming Techniques
    • A.3. Algorithm Pseudocode and Code Examples
    • A.4. References and Further Reading
    • A.5. Index

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