Author archive for Domino, page 7

Scott Murdoch, PhD, Director of Data Science at HealthJoy, presents how data scientists can use distribution and modeling techniques to understand the pitfalls in their data and avoid making decisions based on dirty data.

Gene Leynes (Senior Data Scientist) and Nick Lucius (Advanced Analytics) from the City of Chicago discussed two predictive analytics projects that forecasted potential risk involved with E. coli in Lake Michigan and West Nile Virus from mosquitos.

Mac Rogers, Research Engineer at Domino, presented best practices for creating Jupyter dashboards at a recent Domino Data Science Pop-Up.

Jennifer Shin is the Founder & Chief Data Scientist at 8 Path Solutions. Jennifer is on the faculty in the data science graduate program at UC Berkeley, on the Advisory Board for the M.S. in Data Analytics program at the City University of New York, and teaches business analytics and data visualization in the graduate program at NYU. At a Data Science Popup, Jennifer delivered an introduction to approximate string matching, or fuzzy matching. This blog post covers a session summary, a video of the entire session, and a video transcript of the presentation. If you are interested in attending a future Data Science Popup, the next event is November 14th in Chicago.

Max Shron, the head of data science at Warby Parker, delivered a presentation on stakeholder-driven data science at a Data Science Popup. This blog post provides a session summary, a video of the entire session, and a video transcript of the presentation. If you are interested in attending a future Data Science Popup, the next event is November 14th in Chicago.

Nick Kolegraff is the founder of Whiteboard, a strategic innovation company focused on machine learning and AI. Previously, Kolegraff was the Chief Data Scientist at Rackspace and a Principal Data Scientist at Accenture. As a part of Domino’s “Data Science Leaders at Work” guest blogger series, Kolegraff provides advice for data scientists and data science managers to consider when, or if, they decide to take a “chief data scientist” role. This advice includes insights on the mindset you need to have, the types of problems you need to solve, and the people you need to hire. There are three posts total. This post focuses on the mindset you need to have to be a “Chief Data Scientist”.