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hadley
2016-07-22 08:57:07 -05:00
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@@ -65,18 +65,20 @@ The modelling chapters are even more opinionated than the rest of the book. I ap
* *Statistical Modeling: A Fresh Approach* by Danny Kaplan,
<http://www.mosaic-web.org/go/StatisticalModeling/>. This book provides
a gentle introduction to modelling, where you build your intuition,
mathematical tools, and R skills in parallel.
mathematical tools, and R skills in parallel. The book replaces a traditional
"introduction to statistics" course, providing a curriculum that is up-to-date
and relevant to data science.
* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten,
* *An Introduction to Statistical Learning* by Gareth James, Daniela Witten,
Trevor Hastie, and Robert Tibshirani, <http://www-bcf.usc.edu/~gareth/ISL/>
(available online for free). This book presents a family of modern modelling
techniques collectively known as statistical learning.
techniques collectively known as statistical learning. For an even deeper
understanding of the math behind the models, read the classic
*Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani, and
Jerome Friedman, <http://statweb.stanford.edu/~tibs/ElemStatLearn/> (also
available online for free).
* *Applied Predictive Modeling* by Max Kuhn and Kjell Johnson,
<http://appliedpredictivemodeling.com>. This book is a companion to the
__caret__ package, and provides practical tools for dealing with real-life
predictive modelling challenges.
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For much of the insirpiration of the visualisations of these models, and extensions to more complex families, you might like "MODEL VIS PAPER"
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