Using Python and Neo4j for Data Analytics

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. Speaker: Nicole White (@_nicolemargaret), Data Scientist at Neo4j