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Data Structure is extremely important in order to interface programming with real-time problems. For an example, what can be better than graph if anyone is interested in analyzing a real- life problem and proceed towards solving it? Data Structure includes many additional features to reach the epitome of effectiveness and such features are the stack, queue, linked lists and much more. These basic concepts which originate from this domain have their usage in other fields also. This one step ahead system of programming may appear to be complex, however, if a user is able to realize its usage and if he or she is able to find a clue to this puzzle, Data Structure can be more of a fun game or trick. This course is a well-defined way to learn and make a proper grip towards this field, algorithms are necessary at almost every level and thus this course is necessary for making the fundamentals clear. Prerequisite: Basics of any Programming Language.


Chapter 1 - Introduction
1.3 A Brief Introduction to Recursion
1.4 C++ Classes
1.5 C++ Details
1.6 Templates
1.7 Using Matrices

Chapter 2 - Algorithm Analysis
2.1 Mathematical Background
2.2 Model
2.3 What to Analyze
2.4 Running Time Calculations

Chapter 3 - Lists, Stacks, and Queues
3.1 Abstract Data Types (ADTs)
3.2 The List ADT
3.3 vector and list in the STL
3.4 Implementation of vector
3.5 Implementation of list
3.6 The Stack ADT
3.7 The Queue ADT

Chapter 4 - Trees
4.1 Preliminaries
4.2 Binary Trees
4.3 The Search Tree ADT Binary Search Trees
4.4 AVL Trees
4.5 Splay Trees
4.6 Tree Traversals (Revisited)
4.7 B-Trees
4.8 Sets and Maps in the Standard Library

Chapter 5 - Hashing
5.1 General Idea
5.2 Hash Function
5.3 Separate Chaining
5.4 Hash Tables Without Linked Lists
5.5 Rehashing
5.6 Hash Tables in the Standard Library
5.7 Extendible Hashing

Chapter 6 - Priority Queues (Heaps)
6.1 Model
6.2 Simple Implementations
6.3 Binary Heap
6.4 Applications of Priority Queues
6.5 d-Heaps
6.6 Leftist Heaps
6.7 Skew Heaps
6.8 Binomial Queues
6.9 Priority Queues in the Standard Library

Chapter 7 - Sorting
7.1 Preliminaries
7.2 Insertion Sort
7.3 A Lower Bound for Simple Sorting Algorithms
7.4 Shellsort
7.5 Heapsort
7.6 Mergesort
7.7 Quicksort
7.8 Indirect Sorting
7.9 A General Lower Bound for Sorting
7.10 Bucket Sort
7.11 External Sorting

Chapter 9 - Graph Algorithms
9.1 Definitions
9.2 Topological Sort
9.3 Shortest-Path Algorithms
9.4 Network Flow Problems
9.5 Minimum Spanning Tree
9.6 Applications of Depth-First Search
9.7 Introduction to NP-Completeness

Chapter 10 - Algorithm Design Techniques
10.1 Greedy Algorithms
10.2 Divide and Conquer
10.3 Dynamic Programming
10.4 Randomized Algorithms
10.5 Backtracking Algorithms

Chapter 11 - Amortized Analysis
11.1 An Unrelated Puzzle
11.2 Binomial Queues
11.3 Skew Heaps
11.4 Fibonacci Heaps
11.5 Splay Trees

Chapter 12 - Advanced Data Structures and Implementation
12.1 Top-Down Splay Trees
12.2 Red-Black Trees
12.3 Deterministic Skip Lists
12.4 AA-Trees
12.5 Treaps
12.6 k-d Trees
12.7 Pairing Heaps