Nina Zumel has updated our training page to describe the Python data science intensive for software engineers we have been conducting for a couple of years. This is private group training in addition to our usual R training for scientists, and consulting offerings. Please check it out.
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Allison Horst, Alison Hill, and Kristen Gorman are working to make a neat new example data set available to R users: the palmer penguins. It is a nice alternative to the over-used Iris data set as it has more rows, some missing values, nicer examples of Simpson’s Paradox, and more […]
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Nina and I are cleaning up websites, links, and projects. I would like to take the opportunity re-share my old genetic art project through a short demonstration video. Read more about the Genetic Art Project here.
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Chapter 8 “Advanced Data Preparation” of Practical Data Science with R is a study in: Using the R vtreat package for advanced data preparation. Cross-validated data preparation. It is the professionally edited, ready to cite version of an important data preparation methodology. An advantage being: a number of well documented […]
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One of the chapters that we are especially proud of in Practical Data Science with R is Chapter 7, “Linear and Logistic Regression.” We worked really hard to explain the fundamental principles behind both methods in a clear and easy-to-understand form, and to document diagnostics returned by the R implementations […]
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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 […]
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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.
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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!
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