[Pluralsight] An Introduction to Algorithmics

By Rasmus Amossen

An introductory guided tour to the field of data structures, algorithms, and complexity analysis. This course is loaded with a ton of practical examples, and focuses on intuition, rather than formulas and mathematical proofs.

Table of contents
* Introduction to Algorithms 7m 27s
Strategies Matter 7m 27s
* Measuring Performance 57m 39s
Introduction 8m 18s
Asymptotic Performance 5m 8s
Big Theta 9m 36s
Big O 11m 46s
Big Omega 2m 40s
Recursive Methods 13m 57s
Amortized Complexity 2m 27s
Lessons Learned 3m 44s
* Organizing Data Efficiently with Common Data Structures 58m 53s
Introduction 9m 39s
Dynamic Arrays 10m 25s
Linked Lists 8m 28s
Priority Queues 10m 21s
Hash Tables 15m 43s
Lessons Learned 4m 15s
* Operating on Data Efficiently with Common Algorithms 1h 31m
Introduction 2m 34s
Graph Traversal 14m 47s
Brute Force and Greedy Algorithms 8m 36s
Divide and Conquer 12m 21s
Dynamic Programming 15m 22s
Dynamic Programming 224m 23s
Branch and Bound 18m 32s
Lessons Learned 4m 46s
* Looking Ahead to Some Very Hard Problems 34m 36s
Introduction 1m 37s
Input Size in Bits 5m 46s
P vs. NP 1 10m 48s
P vs. NP 2 4m 54s
Heuristics and Approximation Algorithms 7m 21s
Lessons Learned 4m 7s

Description
The phrase "Get Great Performance for Free!" sounds like a quote from bad commercial, but when it comes to algorithms and data structures, that may actually be the case. This introductory course shows how the use of common data structures may simplify and even significantly impact performance of solutions to typical real-life everyday programming problems. The course gently introduces the viewer for "complexity analysis" which makes it possible to spot a poorly (and a great) performing program, even without the need for executing it. Complexity analysis is an invaluable tool or "language" for discussing performance with colleagues - and it's not even difficult. After having covered the most common data structures, the course continues to describe some general strategies (algorithms) to efficiently solve more high-level problems. Like with data structures, it is shown how a careful choice of problem solving strategy can dramatically reduce computation time. The last part of the course shifts the focus a bit and shortly teases a few popular theoretical subjects and explains, at a purely intuitive level, what the complexity classes P, NP, and the famous problem, P = NP, is all about.