Data Science Recommended Books

7/25/2019 Data Science Recommended Books

1/23

Recommended BookIntroduction to Data Science

Nina Zumel

John Mount


2/23

Lesson Goals

Present an overview of a number of helpful and re

books

(You certainly do not need to buy all, or even any obut it is good to know they are available)


3/23

R


4/23

Hands-On Programming

with RGarrett Grolemund

A basic introduction to programming and R,using a project-oriented approach.


5/23

R for EveryoneJared P. Lander

Well-liked, very popular R book.

Covers programming basics and basic Rstructures. Good survey of basic statistics and

modeling in R.


6/23

R in Action, Second EditionRobert I. Kabacoff

Our go-to R reference.

Covers R programming structures, datamanagement and statistical functions. Less machinelearning than Lander, but comprehensive coverage

of classical statistical analysis as done in R.


7/23

The Art of R ProgrammingNorman Matloff

Discusses R from a Computer Science(programming) rather than Statistical

perspective. Includes a chapter on Rs debuggingenvironment very useful!


8/23

Advanced RHadley Wickham

Good deep dive into the internals of R as aprogramming language. A must if you are

interested in developing R packages.

Also available online: http://adv-r.had.co.nz
http://adv-r.had.co.nz/


9/23

Reproducible Research

with R and RStudioChristopher Gandrud

Covers R markup and knitrwithin the RStudioenvironment.

Good introduction to the tool ecosystemaround R, and gives good advice on setting up

an R-based project data science project.


10/23

R Graphics CookbookWinston Chang

Useful collection of recipes for creating graphicsin R, primarily in ggplot2.


11/23

SQL


12/23

SQL for Smarties, Fourth

EditionJoe Celko

Our go-to SQL reference.


13/23

Statistics


14/23

Statistics, Fourth Edition

David Freedman, Robert Pisani,

Roger PurvesGood introduction to Statistics and Probability.

Intuitive approach that relies on illustrations andexamples, rather than formulas.


15/23

A Handbook of Statistical Analyses

using R

Torsten Hothorn,

Brian S. Everitt

Intermediate level (assumes familiarity with Rand basic statistics).

Covers several techniques in statistical dataanalysis, including linear and logistic regression,cluster analysis, and multidimensional scaling.


16/23

Theory


17/23

The Elements of Statistical

Learning, 2nd edition

Trevor Hastie, Robert Tibshirani,

Jerome Friedman

Our go-to machine learning reference.

Covers a wide variety of statistical machinelearning algorithms, including their derivation,

strengths and weaknesses.


18/23

Practice


19/23

An Introduction to Statistical

Learning

Gareth James, Daniela Witten,

Trevor Hastie, Robert Tibshirani

Companion text to The Elements of StatisticalLearning.

More application oriented, with example codein R.


20/23

Applied Predictive

ModelingMax Kuhn, Kjell Johnson

Practice-oriented approach to predictivemodeling.

Good combination of theory and practice. Alsocovers data treatment, model evaluation, and

feature selection. Example code in R.


21/23

Practical Data Science with R

Nina Zumel,John Mount

Our book!A practitioner-oriented view of data science.

Covers all aspects of a data science project: projectmanagement, data treatment, modeling and modelevaluation, deployment and reporting. Extensive

example code in R. M A N N I N G

Nina Zumel

John Mount

FOREWORD BY Jim Porzak


22/23

And more

More good books (our list is not comprehensive)

Some web resources:

http://www.r-bloggers.com

http://www.statmethods.net (Quick-R)

http://cran.r-project.org
http://cran.r-project.org/http://www.statmethods.net/http://www.r-bloggers.com/


23/23

What you should take aw

There are a lotof available resources

Research what you actually need and you may need t

one or two extra books.

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Data Science Recommended Books

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