Learning Data Structures and Algorithms

Learning Data Structures and Algorithms
Learning Data Structures and Algorithms

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 8 Hours | 0.98 GB
eLearning | Skill level: Beginner | by Rod Stephens


Implementation and Analysis for Increased Software Performance

In this Learning Data Structures and Algorithms training course, expert author Rod Stephens will teach you how to analyze and implement common algorithms used. This course is designed for the absolute beginner, meaning no previous programming experience is required.

You will start by learning about the complexity theory, then jump into learning about numerical algorithms, including randomizing arrays, prime factorization, and numerical integration. From there, Rod will teach you about linked lists, such as singly linked lists, sorted, and doubly linked lists. This video tutorial also covers arrays, stacks and queues, and sorting. You will also learn about searching, hash tables, recursion, and backtracking algorithms. Finally, you will cover trees, balanced trees, decision trees, and network algorithms.

Once you have completed this computer based training course, you will be fully capable of analyzing and implementing algorithms, as well as be able to select the best algorithm for various situations. Working files are included, allowing you to follow along with the author throughout the lessons.

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Table of Contents

1. Introduction
Introduction And Course Overview
About The Author
How To Access Your Working Files

2. Complexity Theory
Complexity Theory
Big O Notation
Typical Runtime Functions
Comparing Runtime Functions
P And NP

3. Numerical Algorithms
Random Numbers
Linear Congruential Generators
Randomizing Arrays – Part 1
Randomizing Arrays – Part 2
GCD
LCM
Prime Factorization – Part 1
Prime Factorization – Part 2
Finding Primes
Testing Primality
Numerical Integration

4. Linked Lists
Singly Linked Lists – Part 1
Singly Linked Lists – Part 2
Sorted Linked Lists
Sorting With Linked Lists
Doubly Linked Lists

5. Arrays
One-Dimensional Arrays
Triangular Arrays – Part 1
Triangular Arrays – Part 2
Sparse Arrays – Part 1
Sparse Arrays – Part 2

6. Stacks And Queues
Stacks
Stack Algorithms
Double Stacks
Queues

7. Sorting
Sorting Algorithms
Insertionsort
Selectionsort
Quicksort – Part 1
Quicksort – Part 2
Heapsort – Part 1
Heapsort – Part 2
Heapsort – Part 3
Mergesort – Part 1
Mergesort – Part 2
Bubblesort – Part 1
Bubblesort – Part 2
Countingsort – Part 1
Countingsort – Part 2
Sorting Summary

8. Searching
Linear Search
Binary Search
Interpolation Search

9. Hash Tables
Hash Tables
Chaining
Open Addressing – Basics
Open Addressing – Linear Probing
Open Addressing – Quadratic Probing
Open Addressing – Double Hashing

10. Recursion
Recursion Basics
Fibonacci Numbers
Tower Of Hanoi
Koch Curves
Hilbert Curves
Gaskets
Removing Tail Recursion
Removing Recursion With Stacks
Fixing Fibonacci
Selections
Permutations

11. Backtracking Algorithms
Backtracking
The Eight Queens Problem – Part 1
The Eight Queens Problem – Part 2
The Eight Queens Problem – Part 3
The Knights Tour

12. Trees
Tree Terms
Binary Tree Properties
Traversals – Preorder
Traversals – Postorder
Traversals – Inorder
Traversals – Breadth-First
Building Sorted Trees
Editing Sorted Trees

13. Balanced Trees
Why Do You Need Balanced Trees?
B-Trees – B-Tree Basics
B-Trees – Adding Items
B-Trees – Removing Items
AVL Tress – Part 1
AVL Tress – Part 2

14. Decision Trees
Definition
Exhaustive Search
Branch And Bound
Heuristics

15. Network Algorithms
Network Terminology
Network Classes
Depth-First Traversal
Breadth-First Traversal
Spanning Trees – Part 1
Spanning Trees – Part 2
Shortest Paths – Part 1
Shortest Paths – Part 2
All Pairs Shortest Path – Part 1
All Pairs Shortest Path – Part 2

16. Wrap-Up
Wrap-U