Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you! Learn more about using network science to tackle your data problems with the PyData tools.

## Abstract

This workshop is for data scientists and other programmers who want to add another tool in their data science toolkit. Modelling, analysing and visualising data as networks! Network Science deals with analysing network data, and the data can come from different fields like politics, finance, computer science, law and even Game of Thrones!

In this workshop we will cover the basics of network theory and network thinking, then we will go over some algorithms used to analyse network data. We will take a quick detour to understand the story between linear algebra and networks. We will then jump on to some real world datasets and how to use our newly acquired skills to tackle the data problems.

This will be a hands-on and interactive tutorial, so get ready to code your way till the end!

By the end of the workshop you should be comfortable with working with network data using the PyData ecosystem (NetworkX, pandas, numpy!).

We will roughly follow the following timeline during the workshop.

Part A: Introduction to Graphs and the NetworkX API

Part B: Graph Algorithms

- Hubs: Which nodes are the important nodes in our data?
- Paths: Where should I jump next to find my destination?
- Structures: Who should I be friends with?

Part C: Linear Algebra and Network Science

- What do matrices have to do with nodes and edges?

Part D: Case Studies

- Use Game of Thrones character cooccurrences data and US flight dataset to test out our new skills