Buy it for your favorite data scientist in time for the holidays!
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.
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 author Dr. Nina Zumel, with a fresh author’s copy of her book!
I thought I would give a personal update on our book: Practical Data Science with R 2nd edition; Zumel, Mount; Manning 2019.
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.
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
wrapr). A second edition also lets us also correct some omissions, such as not demonstrating
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 ago today authors Nina Zumel and John Mount received our author’s copies 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.
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.
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.