Six Lists of Lists for Data Scientists

logoData sciences relies on a strong foundation of mathematics, statistics, and results visualization, most of which are available through R statistical programming ecosystems.  To master the data sciences, one needs to delve into some of the more important pieces of literature (spending 10,000 hours) . But what does one read and when?

While many have tried, it is impractical define the definitive list of R resources, given all the great blogs, texts, and videos available. Most attempt to create such a list are failures from the start. So, in many cases, one needs just to Google the phrase “R Resources” in order to find 80% of the good ones, while exerting less than 20% of your overall research effort.

For my list, here are the texts and PDFs that I keep near or with me most of the time:

General introductions to R
1.  An introduction to R. Venables and Smith (2009) – PDF
2.  A beginner’s guide to R (Use R!). Zuur et al. (2009) – Text
3.  R for Dummies. Meys and de Vries (2012) – Text
4.  The R book. Crawley (2012)
5.  R in a nutshell: A desktop quick reference. Adler (2012)

Statistics books
1.  Statistics for Dummies. Gotelli and Ellison (2012) – Text
2.  Statistical methods. Snedecor and Cochran (2014) – Text
3.  Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis. Reid (2013) – Text

Statistics books specifically using R
1.  Introductory statistics: a conceptual approach using R. Ware et al. (2012) – Text
2.  Foundations and applications of statistics: an introduction using R. Pruim (2011) – Text
3.  Probability and statistics with R, 2nd Edition. Ugarte et al. (2008) – Text

Visualization using R
1.  ggplot2: elegant graphics for data analysis. Wickham (2009)
2.  R graphics cookbook. Chang (2013)

Programming using R
1.  The art of R programming. Matloff (2011)
2. Mastering Data Analysis with R. Daroczi (2015)

Interesting predictive analytics books
1. The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t. Silver (2012)
2. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Siegel (2013)

FIELD NOTE: Predictive Apps


Mike Gualtieri (Forrester) believes that “developers are stuck in a design paradigm that reduces app design to making functionality and content decisions based on a few defined customer personas or segments.”

The answer to developing apps that dazzle the digital consumer and making your company stand out from the competition lies in what Gualtieri calls Predictive Apps. Forrester defines predictive apps as:

Apps that leverage big data and predictive analytics to anticipate and provide the right functionality and content on the right device at the right time for the right person by continuously learning about them.

To build anticipatory, individualized app experiences, app developers will use big data and predictive analytics to continuously and automatically tune the app experience by:

  • Learning who the customer (individual user) really is
  • Detecting the customer’s intent in the moment
  • Morphing functionality and content to match the intent
  • Optimizing for the device (or channel)