Please share: Manning Deal of the Day November 19: Half off Practical Data Science with R. Use code dotd1119au at www.manning.com/zumel/.
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. Read more…
Just spoke at The Berkeley R Language Beginner Study Group. Great audience, very bright. Here are my slides: StartR.pdf .
I’ll be talking at the “Official” BARUG meeting Wednesday, August 21, 2013. The RSVPs look full (sorry) but I wanted to post a thanks to the organizers for considering me. If things go well I’ll see if I can post the slides later (not sure if that is useful without detailed speakers notes). Read more…
Deal of the Day August 1: Half off my book Practical Data Science with R. Use code dotd0801au at www.manning.com/zumel/
A bit about our upcoming book “Practical Data Science with R”. Nina and I share our current draft of the front matter from the book, which is a description which will help you decide if this is the book for you (we hope that it is). Or this could be the book that helps explain what you do to others.
We recently got this question from a subscriber to our book:
… will you in any way describe what subject areas, backgrounds, courses etc. would help a non data scientist prepare themselves to at least understand at a deeper level why they techniques you will discuss work…and also understand the boundary conditions and limits of the models etc….. ?
[…] I would love to understand what I could review first to better prepare to extract the most from it.
It’s a good question, and it raises an interesting philosophical point. To read our book, it will of course help to know a little bit about statistics and probability, and to be familiar with R and/or with programming in general. But we do plan on introducing the necessary concepts as needed into our discussion, so we don’t consider these subjects to be “pre-requisites” in a strict sense.
Part of our reason for writing this book is to make reading about statistics/probability and machine learning easier. That is, we hope that if you read our book, other reference books and textbooks will make more sense, because we have given you a concrete context for the abstract concepts that the reference books cover.
So, my advice to our subscriber was to keep his references handy as he read our book, rather than trying to brush up on all the “pre-requisite” subjects first.
Of course, everyone learns differently, and we’d like to know what other readers think. What (if anything) would you consider “pre-requisites” to our book? What would you consider good companion references?
If you are subscribed to our book, please join the conversation, or post other comments on the Practical Data Science with R author’s forum. Your input will help us write a better book; we look forward to hearing from you.
We have some great news for “Practical Data Science with R”:
Nina Zumel and I ( John Mount ) have been working very hard on producing an exciting new book called “Practical Data Science with R.” The book has now entered Manning Early Access Program (MEAP) which allows you to subscribe to chapters as they become available and give us feedback before the book goes into print.
Please subscribe to our book, your support now will help us improve it. Please also forward this offer to your friends and colleagues (and please ask them to also subscribe and forward). Read more…
Dear readers,
I am asking for your help promoting Win Vector LLC and the Win Vector LLC blog ( http://www.win-vector.com/blog/ ). We here at Win Vector LLC try hard to provide quality content and always benefit from more contacts and readers.
If you have any possible leads or can make any introductions to companies that may want some data science consulting I would love to hear from you (email: contact@win-vector.com ).
Also, please subscribe to our data science blog (RSS: http://www.win-vector.com/blog/feed/) and new Twitter account ( http://twitter.com/WinVectorLLC/ ). Better yet please share our blog and Twitter account with anybody you think would be interested (and please ask them to do the same).
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