Data analysis of World Championship

danielwhitenackHear from Daniel Whitenack, Data Scientist and Lead Developer Advocate at Pachyderm. He’ll analyze and visualize chess game data from the recent 2016 chess championship, and will use distributed data pipelines, obscure chess engines, and a variety of data science tooling to get the job done. In the end, you’ll gain some per game and championship insights along with some intuition about each of the players’ strengths and weaknesses.

Happening: Thursday, January 19, 2017, 6:30 PM to 8:00 PM

meetup-logoClick link here for reservation.

Click link here for Google map of location: 1033 West Van Buren St, 3rd Floor, Chicago, IL 60607

Event Schedule: 
6:30 – 7:00: Arrive, enjoy food & drinks
7:00 – 7:30: Daniel presents
7:30 – 8:00: Q&A + networking

Daniel (@dwhitena) is a Ph.D.-trained data scientist working with Pachyderm (@pachydermIO), where he develops innovative, distributed data pipelines that include predictive models, data visualizations, statistical analyses, and more. He’s spoken at conferences around the world (Datapalooza, DevFest Siberia, GopherCon, and more), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

Two more GM Lombardy videos posted.

2016 chess world championship games 3 and 15 discussed: Click for Game 3 (He believes it should have been played out for a possible win.) and Game 15 (He believes there was an obvious draw.).