Practical Data Science with R 2nd Edition is now in-stock at Amazon.com!
Buy it for your favorite data scientist in time for the holidays!
Tag: Practical Data Science
Practical Data Science with R, 2nd Edition: Introduction Video
Nina and I have prepared a quick introduction video for Practical Data Science with R, 2nd Edition.
We are really proud of both editions of the book. This book can help an R user directly experience the data science style of working with data and machine learning techniques.
The book is available now at:
- Directly from the publisher Manning, now (often with significant discounts!).
- Via pre-order from Amazon.com.
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Get a signed copy off us! We will be giving away some e-copies and a few signed physical copies at various conferences and meet-ups
(for example at PyData LA 2019).
Please check it out!
Practical Data Science with R, 2nd Edition, IS OUT!!!!!!!
Practical Data Science with R, 2nd Edition author Dr. Nina Zumel, with a fresh author’s copy of her book!
Practical Data Science with R Book Update (April 2019)
I thought I would give a personal update on our book: Practical Data Science with R 2nd edition; Zumel, Mount; Manning 2019.
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Practical Data Science with R Book Update
A good friend shared with us a great picture of Practical Data Science with R, 1st Edition hanging out in Cambridge at the MIT Press Bookstore.
This is as good an excuse as any to share a book update.
PDSwR2 Free Excerpt and New Discount Code
Manning has a new discount code and a free excerpt of our book Practical Data Science with R, 2nd Edition: here.
This section is elementary, but things really pick up speed as later on (also available in a paid preview).
More Practical Data Science with R Book News
Some more Practical Data Science with R news.
Practical Data Science with R is the book we wish we had when we started in data science. Practical Data Science with R, Second Edition is the revision of that book with the packages we wish had been available at that time (in particular vtreat, cdata, and wrapr). A second edition also lets us also correct some omissions, such as not demonstrating data.table.
For your part: please help us get the word out about this book. Practical Data Science with R, Second Edition, R in Action, Second Edition, and Think Like a Data Scientist are Manning’s August 20th 2018 “Deal of the Day” (use code dotd082018au at https://www.manning.com/dotd).
For our part we are busy revising chapters and setting up a new Github repository for examples and code and other reader resources.
Four Years of Practical Data Science with R
Four years ago today authors Nina Zumel and John Mount received our author’s copies of Practical Data Science with R!
Continue reading Four Years of Practical Data Science with R
Hangul/Korean edition of Practical Data Science with R!
Excited to see our new Hangul/Korean edition of “Practical Data Science with R” by Nina Zumel, John Mount, translated by Daekyoung Lim.
Continue reading Hangul/Korean edition of Practical Data Science with R!
Using PostgreSQL in R: A quick how-to
The combination of R plus SQL offers an attractive way to work with what we call medium-scale data: data that’s perhaps too large to gracefully work with in its entirety within your favorite desktop analysis tool (whether that be R or Excel), but too small to justify the overhead of big data infrastructure. In some cases you can use a serverless SQL database that gives you the power of SQL for data manipulation, while maintaining a lightweight infrastructure.
We call this work pattern “SQL Screwdriver”: delegating data handling to a lightweight infrastructure with the power of SQL for data manipulation.
Image: Iainf, some rights reserved.We assume for this how-to that you already have a PostgreSQL database up and running. To get PostgreSQL for Windows, OSX, or Unix use the instructions at PostgreSQL downloads. If you happen to be on a Mac, then Postgres.app provides a “serverless” (or application oriented) install option.
For the rest of this post, we give a quick how-to on using the RpostgreSQL package to interact with Postgres databases in R.