Practical Data Science with R October 2013 update

A quick status update on our upcoming book “Practical Data Science with R” by Nina Zumel and John Mount.

We are really happy with how the book is coming out. We were able to cover most everything we hoped to. Part 1 (especially chapter 3) is already being used in courses, and has some very good stuff on how to review data. Part 2 covers the “statistical / machine-learning canon,” and turns out to be a very complete demonstration of what odd steps are needed to move from start to finish for each example in R. Part 3 is going to finish with the important (but neglected) topics of delivering results to production, and building good documentation and presentations.

Some detailed updates:

• The free preview of chapter 1 has been expanded to include the book introduction and front-matter.
• The (paid subscription) Manning MEAP has been updated with chapters one through seven now available (and revised!).
• The book’s free GitHub repository now includes preparation steps for ten significant data science projects and a zip-file of all listing examples from book.
• We are still having some issues with section numbering with the draft tools (this will not be a problem in the final book, which is to be professionally copy-edited and produced by different tools).
• Both authors are starting to do more speaking engagements and tweeting (as WinVectorLLC). We would very much appreciate it if you would subscribe to our Tweet-stream and encourage others to do so (and to also follow our blog). We are really going to need your help in publicizing our book.

Thanks!

Related posts:

1. Data science project planning
2. Data Science, Machine Learning, and Statistics: what is in a name?
3. Setting expectations in data science projects