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An intuition-first approach

What you will 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?


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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.

English
language

Content

Introduction
Motivation and history
Definitions
Definitions 2
Paths
Depth First Search
DFS Example
DFS Pseudocode
Breadth First Search
Dijkstra’s algorithm
Eulerian Paths
Review
Connections
Intro
DFS Tree
Connectivity
Connectivity 2: Articulation points with a DFS Tree
Connectivity 3: Tarjan’s algorithm for strongly connected components
Centrality introduction
Degree centrality
Closeness centrality
Betweenness centrality
Betweenness centrality 2
Review
Community
Density
Minimum spanning tree
Minimum spanning tree- Union Find
Kruskal’s algorithm
Modularity
Louvain Algorithm
Review