We have an exciting new article to share: Don’t Feel Guilty About Selecting Variables. If you are at all interested in the probabilistic justification of important data science techniques, such as variable selection or pruning, this should be an informative and fun read. “Data Science” is often criticized with the […]
A kind reader recently shared the following comment on the Practical Data Science with R 2nd Edition live-site. Thanks for the chapter on data frames and data.tables. It has helped me overcome an obstacle freeing me from a lot of warnings telling me my data table was not a real […]
Data science is often a case of brining the tools to the problems and data, instead of insisting on bringing the problems and data to the tools. To support cross-language data science we have been working on cross-language tools, documentation, and training.
Deal of the Day May 10: Half off Practical Data Science with R, Second Edition. Use code dotd051020au at https://bit.ly/2xLRPCk
Thank you very much Why R? for being awesome hosts. We are really pleased with how your virtual MeetUp went. For those who missed it here is a video link.
Nina Zumel and John Mount will be speaking on advanced data preparation for supervised machine learning at the Why R? Webinar Thursday, May 7, 2020. This is a 8pm in a GMT+2 timezone, which for us is 11AM Pacific Time. Hope to see you there!
Here are a few isolation inspired “applications” (in the theoretical or mathematical sense of the term) of the spicy soup combinatorial design.
Here is a fun combinatorial puzzle. I’ve probably seen this used to teach before, but let’s try to define or work this one from memory. I would love to hear more solutions/analyses of this problem. Suppose you have n kettles of soup labeled 0 through n-1. For our problem we […]
We have a discount on Manning Books, including our own Practical Data Science with R 2nd Edition!
Win Vector LLC’s Dr. Nina Zumel has had great success applying y-aware methods to machine learning problems, and working out the detailed cross-validation methods needed to make y-aware procedures safe. I thought I would try our hand at y-aware neural net or deep learning methods here.