data science recommended books
TRANSCRIPT
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7/25/2019 Data Science Recommended Books
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Recommended BookIntroduction to Data Science
Nina Zumel
John Mount
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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)
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R
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Hands-On Programming
with RGarrett Grolemund
A basic introduction to programming and R,using a project-oriented approach.
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7/25/2019 Data Science Recommended Books
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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.
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7/25/2019 Data Science Recommended Books
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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.
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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!
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7/25/2019 Data Science Recommended Books
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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/ -
7/25/2019 Data Science Recommended Books
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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.
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7/25/2019 Data Science Recommended Books
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R Graphics CookbookWinston Chang
Useful collection of recipes for creating graphicsin R, primarily in ggplot2.
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7/25/2019 Data Science Recommended Books
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SQL
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7/25/2019 Data Science Recommended Books
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SQL for Smarties, Fourth
EditionJoe Celko
Our go-to SQL reference.
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Statistics
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Statistics, Fourth Edition
David Freedman, Robert Pisani,
Roger PurvesGood introduction to Statistics and Probability.
Intuitive approach that relies on illustrations andexamples, rather than formulas.
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7/25/2019 Data Science Recommended Books
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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.
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Theory
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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.
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Practice
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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.
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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.
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7/25/2019 Data Science Recommended Books
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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
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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/ -
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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.