Network Analysis Made Simple
Network Analysis Made Simple


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. In this tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

This tutorial is for Pythonistas who want to understand relationship problems - as in, data problems that involve relationships between entities. Participants should already have a grasp of for loops and basic Python data structures (lists, tuples and dictionaries). By the end of the tutorial, participants will have learned how to use the NetworkX package in the Jupyter environment, and will become comfortable in visualizing large networks using Circos plots. Other plots will be introduced as well.


Eric is an Investigator at the Novartis Institutes for Biomedical Research, where he solves biological problems using machine learning. He obtained his Doctor of Science (ScD) from the Department of Biological Engineering, MIT, and was an Insight Health Data Fellow in the summer of 2017. He has taught Network Analysis at a variety of data science venues, including PyCon USA, SciPy, PyData and ODSC, and has also co-developed the Python Network Analysis curriculum on DataCamp. As an open source contributor, he has made contributions to PyMC3, matplotlib and bokeh. He has also led the development of the graph visualization package nxviz, and a data cleaning package pyjanitor (a Python port of the R package).

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