Grokking Graph Analytics and Algorithms

Grokking Graph Analytics and Algorithms
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 905 MB

Genre: eLearning Video | Duration: 28 lectures (3 hour, 12 mins) | Language: English [/center]

An intuition-first approach

What you'll learn

Grok graph data structures
Convert real world systems in to graph data structures
Develop an intuitive understanding of fundamental algorithms for analyzing and understanding graphs
Build a toolbox of algorithms to use in data analytics tasks

Description

What is a graph?

A Graph is a collection of Nodes and Edges. The nodes represent entities, such as people, computers in a network, or molecules in a chemical reaction. The edges represent the relationships between them such as friendships (or frenemies), direct connections, or constituents in a reaction.

Graph databases are online systems that let people manage graph data. Unlike older databases, priority is given to relationships between entities. This means you don't have to mess around with complicated keys and joins to analyze large portions of a system.

Why are graph databases important?

Graphs are growing in prevalence. Every time you visit Facebook, you're getting information on first, second, and even third-degree connections to you and your friends.

The biggest tech companies around leverage graph data and analytics to understand how users relate to each other, and with the content on their site.

What does this course teach?

This course will provide an intuition-first approach to understanding, analyzing, and manipulating graph data.

I've picked out only the most important algorithms, and build solutions from the ground up using real world examples

Is this course right for me?

This course is intended for students who want to prepare for the workforce, professionals who want to learn more about graph data and keep abreast of new technology, and anyone with a curios mind and desire to learn.
Who this course is for:

Software engineers developing complex systems.
Data wranglers curious about the relationships between entities in their systems.
Machine learning engineers looking to level up their predictions.
Computer science enthusiasts wanting to build/reinforce their graph data structure fundamentals.

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