Graph Databases for Python Users

What's this talk about?

Most data scientists will tell you that they spend the majority of their time cleaning and munging data and only a fraction of their time actually building predictive models. This is true in a traditional stack, where most of this data munging consists of writing some flavor of SQL – a lot of it. And, if the domain is highly-connected, some questions may even be impossible to express in SQL due to its tabular limitations. With the appropriate technology stack, however, a data scientist’s development process is seamless and short: learn how to combine the compact syntax of Python with the flexibility of an open source, schema-less graph database Neo4j to build a data scientist’s optimal open source stack. In this session, you’ll learn how to use Python to collect data from Twitter’s API, Neo4j to easily and reliably store this highly-connected data, and Python again for quick analysis and visualization.

Meet Your Speaker:

Nicole White grew up in Kansas City, Missouri and then spent four years at LSU in Baton Rouge, Louisiana where she got a degree in economics with a minor in mathematics. She then went to the University of Texas at Austin where she got her masters degree in analytics, and it was during this time that she found Neo4j and began playing around with it. When she's not graphing all the things, she spends her time playing card games and board games.

What should I bring?

A laptop if you'd like to follow along

Prerequisites?

Attend our Introduction to Graphs on September 16th! (Or have some working knowledge of graphs)