MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 28 lectures (3h 12m) | Size: 784.7 MB
An intuition-first approach
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
The basics are all developed intuitively, no prior experience necessary
High school level algebra
Some sections (one, really) benefit from linear algebra knowledge
Some familiarity with Python will help you understand the code I write.
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 chal reaction. The edges represent the relationships between them such as friendships (or frens), 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 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.
Software eeers developing complex systems.
Data wranglers curious about the relationships between entities in their systems.
Machine learning eeers looking to level up their predictions.
Computer science enthusiasts wanting to build/reinforce their graph data structure fundamentals.