Schedule: Sponsored sessions

Ian Swanson explores the key components of a data science platform and explains how they are enabling organizations to realize the potential of their data science teams. Read more.

Peter Wang explores open source commercial companies, offering a firsthand account of the unique challenges of building a company that is fundamentally centered around sustainable open source innovation and sharing guidelines for how to carry volunteer-based open source values forward, intentionally and thoughtfully, in a data-centric world. Read more.

Romain Menegaux and Chakri Cherukuri demonstrate how to develop advanced applications and dashboards using open source projects, illustrated with examples in machine learning, finance, and neuroscience. Read more.

Mac Rogers shares best practices for creating Jupyter dashboards and some lesser-known tricks for making Jupyter dashboards interactive and attractive. Read more.

Raj Singh offers an overview of PixieDust, a Jupyter Notebook extension that provides an easy way to make interactive maps from DataFrames for visual exploratory data analysis. Raj explains how he built mapping into PixieDust, putting data from Apache Spark-based analytics on maps using Mapbox GL. Read more.

Pramit Choudhary offers an overview of Datascience.com's model interpretation library Skater, explains how to use it to evaluate models using the Jupyter environment, and shares how it could help analysts, data scientists, and statisticians better understand their model behavior—without compromising on the choice of algorithm. Read more.

Christine Doig offers an overview of the Anaconda Project, an open source library created by Continuum Analytics that delivers lightweight, efficient encapsulation and portability of data science projects. A JupyterLab extension enables data scientists to install the necessary dependencies, download datasets, and set environment variables and deployment commands from a graphical interface. Read more.