Data Structure and Algorithmic Thinking with Python: Data Structure and Algorithmic Puzzles

0. Organization of Chapters
0.1 What Is This Book About?
0.2 Should I Buy This Book?
0.3 Organization of Chapters
0.4 Some Prerequisites
1. Introduction
1.1 Variables
1.2 Data Types
1.3 Data Structures
1.4 Abstract Data Types (ADTs)
1.5 What is an Algorithm?
1.6 Why the Analysis of Algorithms?
1.7 Goal of the Analysis of Algorithms
1.8 What is Running Time Analysis?
1.9 How to Compare Algorithms
1.10 What is Rate of Growth?
1.11 Commonly Used Rates of Growth
1.12 Types of Analysis
1.13 Asymptotic Notation
1.14 Big O Notation
1.15 Omega Ω Notation
1.16 Theta  Notation
1.17 Why is it called Asymptotic Analysis?
1.18 Guidelines for Asymptotic Analysis
1.19 Simplifying properties of asymptotic notations
1.21 Commonly used Logarithms and Summations
1.21 Master Theorem for Divide and Conquer Recurrences
1.22 Divide and Conquer Master Theorem: Problems & Solutions
1.23 Master Theorem for Subtract and Conquer Recurrences
1.24 Variant of Subtraction and Conquer Master Theorem
1.25 Method of Guessing and Confirming
1.26 Amortized Analysis
1.27 Algorithms Analysis: Problems & Solutions
2. Recursion and Backtracking
2.1 Introduction
2.2 What is Recursion?
2.3 Why Recursion?
2.4 Format of a Recursive Function
2.5 Recursion and Memory (Visualization)
2.6 Recursion versus Iteration
2.7 Notes on Recursion
2.8 Example Algorithms of Recursion
2.9 Recursion: Problems & Solutions
2.10 What is Backtracking?
2.11 Example Algorithms of Backtracking
2.12 Backtracking: Problems & Solutions
3. Linked Lists
3.1 What is a Linked List?
3.2 Linked Lists ADT
3.3 Why Linked Lists?
3.4 Arrays Overview
3.5 Comparison of Linked Lists with Arrays and Dynamic Arrays
3.6 Singly Linked Lists
3.7 Doubly Linked Lists
3.8 Circular Linked Lists
3.9 A Memory efficient Doubly Linked List
3.11 Skip Lists
3.12 Linked Lists: Problems & Solutions
4. Stacks
4.1 What is a Stack?
4.2 How Stacks are Used
4.3 Stack ADT
4.4 Applications
4.5 Implementation
4.6 Comparison of Implementations
4.7 Stacks: Problems & Solutions
5. Queues
5.1 What is a Queue?
5.2 How are Queues Used
5.3 Queue ADT
5.4 Exceptions
5.5 Applications
5.6 Implementation
5.7 Queues: Problems & Solutions
6. Trees
6.1 What is a Tree?
6.2 Glossary
6.3 Binary Trees
6.4 Types of Binary Trees
6.5 Properties of Binary Trees
6.6 Binary Tree Traversals
6.7 Generic Trees (N ary Trees)
6.8 Threaded Binary Tree Traversals (Stack or Queue less Traversals)
6.9 Expression Trees
6.10 XOR Trees
6.11 Binary Search Trees (BSTs)
6.12 Balanced Binary Search Trees
6.13 AVL (Adelson Velskii and Landis) Trees
6.14 Other Variations on Trees
6.15 Supplementary Questions
7. Priority Queues and Heaps
7.1 What is a Priority Queue?
7.2 Priority Queue ADT
7.3 Priority Queue Applications
7.4 Priority Queue Implementations
7.5 Heaps and Binary Heaps
7.6 Binary Heaps
7.7 Heapsort
7.8 Priority Queues [Heaps]: Problems & Solutions
8. Disjoint Sets ADT
8.1 Introduction
8.2 Equivalence Relations and Equivalence Classes
8.3 Disjoint Sets ADT
8.4 Applications
8.5 Tradeoffs in Implementing Disjoint Sets ADT
8.8 Fast UNION Implementation (Slow FIND)
8.9 Fast UNION Implementations (Quick FIND)
8.10 Summary
8.11 Disjoint Sets: Problems & Solutions
9. Graph Algorithms
9.1 Introduction
9.2 Glossary
9.3 Applications of Graphs
9.4 Graph Representation
9.5 Graph Traversals
9.6 Topological Sort
9.7 Shortest Path Algorithms
9.8 Minimal Spanning Trees
9.9 Graph Algorithms: Problems & Solutions
10. Sorting
10.1 What is Sorting?
10.2 Why is Sorting Necessary?
10.3 Classification of Sorting Algorithms
10.4 Other Classifications
10.5 Bubble Sort
10.6 Selection Sort
10.7 Insertion Sort
10.8 Shell Sort
10.9 Merge Sort
10.11 Quick Sort
10.12 Tree Sort
10.13 Comparison of Sorting Algorithms
10.14 Linear Sorting Algorithms
10.15 Counting Sort
10.16 Bucket Sort (or Bin Sort)
10.17 Radix Sort
10.18 Topological Sort
10.19 External Sorting
10.20 Sorting: Problems & Solutions
11. Searching
11.1 What is Searching?
11.2 Why do we need Searching?
11.3 Types of Searching
11.4 Unordered Linear Search
11.5 Sorted/Ordered Linear Search
11.6 Binary Search
11.7 Interpolation Search
11.8 Comparing Basic Searching Algorithms
11.9 Symbol Tables and Hashing
11.10 String Searching Algorithms
11.11 Searching: Problems & Solutions
12. Selection Algorithms [Medians]
12.1 What are Selection Algorithms?
12.2 Selection by Sorting
12.3 Partition based Selection Algorithm
12.4 Linear Selection Algorithm   Median of Medians Algorithm
12.5 Finding the K Smallest Elements in Sorted Order
12.6 Selection Algorithms: Problems & Solutions
13. Symbol Tables
13.1 Introduction
13.2 What are Symbol Tables?
13.3 Symbol Table Implementations
13.4 Comparison Table of Symbols for Implementations
14. Hashing
14.1 What is Hashing?
14.2 Why Hashing?
14.3 Hash Table ADT
14.4 Understanding Hashing
14.5 Components of Hashing
14.6 Hash Table
14.7 Hash Function
14.8 Load Factor
14.9 Collisions
14.10 Collision Resolution Techniques
14.11 Separate Chaining
14.12 Open Addressing
14.13 Comparison of Collision Resolution Techniques
14.14 How Hashing Gets O(1) Complexity
14.15 Hashing Techniques
14.16 Problems for which Hash Tables are not suitable
14.17 Bloom Filters
14.18 Hashing: Problems & Solutions
15. String Algorithms
15.1 Introduction
15.2 String Matching Algorithms
15.3 Brute Force Method
15.4 Rabin Karp String Matching Algorithm
15.5 String Matching with Finite Automata
15.6 KMP Algorithm
15.7 Boyer Moore Algorithm
15.8 Data Structures for Storing Strings
15.9 Hash Tables for Strings
15.10 Binary Search Trees for Strings
15.11 Tries
15.12 Ternary Search Trees
15.13 Comparing BSTs, Tries and TSTs
15.14 Suffix Trees
15.15 String Algorithms: Problems & Solutions
16. Algorithms Design Techniques
16.1 Introduction
16.2 Classification
16.3 Classification by Implementation Method
16.4 Classification by Design Method
16.5 Other Classifications
17. Greedy Algorithms
17.1 Introduction
17.2 Greedy Strategy
17.3 Elements of Greedy Algorithms
17.4 Does Greedy Always Work?
17.5 Advantages and Disadvantages of Greedy Method
17.6 Greedy Applications
17.7 Understanding Greedy Technique
17.8 Greedy Algorithms: Problems & Solutions
18. Divide and Conquer Algorithms
18.1 Introduction
18.2 What is Divide and Conquer Strategy?
18.3 Does Divide and Conquer Always Work?
18.4 Divide and Conquer Visualization
18.5 Understanding Divide and Conquer
18.6 Advantages of Divide and Conquer
18.7 Disadvantages of Divide and Conquer
18.8 Master Theorem
18.9 Divide and Conquer Applications
18.10 Finding peak element of an array
18.11 Finding peak element in two dimensional array
18.12 Divide and Conquer: Problems & Solutions
19. Dynamic Programming
19.1 Introduction
19.2 What is Dynamic Programming Strategy?
19.3 Properties of Dynamic Programming Strategy
19.4 Greedy vs Divide and Conquer vs DP
19.5 Can DP solve all problems?
19.6 Dynamic Programming Approaches
19.7 Understanding DP Approaches
19.8 Examples of DP Algorithms
19.9 Longest Common Subsequence
19.10 Dynamic Programming: Problems & Solutions
20. Complexity Classes
20.1 Introduction
20.2 Polynomial/Exponential Time
20.3 What is a Decision Problem?
20.4 Decision Procedure
20.5 What is a Complexity Class?
20.6 Types of Complexity Classes
20.7 Reductions
20.8 Complexity Classes: Problems & Solutions
21. Miscellaneous Concepts
21.1 Introduction
21.2 Hacks on Bit wise Programming
References