Subject archive for "data," page 2

Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine Learning with Python for Everyone by Mark E. Fenner.

Paco Nathan's latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams.

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. Many thanks to Chris Wiggins for providing feedback on this post prior to publication.

Even the most sophisticated data science organizations struggle to keep track of their data science projects. Data science leaders want to know, at any given moment, not just how many data science projects are in flight but what the latest updates and roadblocks are when it comes to model development and what projects need their immediate attention.

Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript.