We are very proud to present our book Practical Data Science with R. This is the book for you if you are a data scientist, want to be a data scientist, or want to work with data scientists. This is a good “what next” book for analysts and programmers wanting to know more about machine learning and data manipulation.
New: SIGACT review of PDsWR!
Our goal is to present data science from a pragmatic, practice-oriented viewpoint. The book will complement other analytics, statistics, machine learning, data science and R books with the following features:
- This book teaches you how to work as a data scientist. Learn how important listening, collaboration, honest presentation and iteration are to what we do.
- The key emphasis of the book is process: collecting requirements, loading data, examining data, building models, validating models, documenting and deploying models to production.
- We provide over 10 significant example datasets, and demonstrate the concepts that we discuss with fully worked exercises using standard R methods. We feel that this approach allows us to illustrate what we really want to teach and to demonstrate all the preparatory steps necessary to any real-world project. Every result and almost every graph in the book is given as a fully worked example.
- This book is scrupulously correct on statistics, but presents topics in the context and order a practitioner worries about them. For example we emphasize construction of predictive models and model evaluation and prediction over the more standard topics of summary statistics and packaged procedures (such as ANOVA).
In support of Practical Data Science with R we are providing:
- Table of contents, and two free example chapters are available from the Manning book page .
- A public repository of data sets (under a Creative Commons Attribution-NonCommercial 3.0 Unported License where possible). This repository has data, sources, notes, and even some ad-hoc R code used to prepare the data for what later becomes the book examples.
- Downloadable example code. This repository includes code and data to work every example in the book, and to reproduce almost every machine generated figure.
- A complete re-run of every example from the book in the form of publicly shared R-markdown. These sheets won’t make a lot of sense without the book, but they are machine executable examples of how to run everything. We regularly update these to keep the book examples up to date as referred to libraries change. Want to see raw code and graphs from the book together without downloading anything, this is the place to look (example: all graphs/results from Chapter 3).
- An official book forum and errata.
The book is available in print as 416 pages softbound black and white or as a color eBook. The print version comes with a complimentary eBook version (an insert when the book is purchased new), in all three formats: PDF, ePub, and Kindle. The eBook can be purchased separately from Manning Publications.
Order now on the Manning book page or at Amazon.com.
Practical Data Science with R by Nina Zumel and John Mount – This book is one of a kind. It moves fluidly between the various stages of the data science process from surface considerations of working with customers to the deep details of various machine learning algorithms. There is quite a bit of original R code that you can use in real projects. Most impressive is the statistical sensibility of the authors who want you to make correct inferences from your data and machine learning models as well as effectively communicate your findings to the people paying the bills.
Joseph Rickert, Rated R: Recommended Reading
For more about the book please check out:
Also, Win-Vector LLC is available to help with your data science projects our training. We would love to work with you.