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<h1 class="title toc-ignore"><em>Modern Data Science with R</em></h1>
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<div id="nd-edition-plus-light-edits-and-updates-published-2024"
class="section level3">
<h3><a href="https://mdsr-book.github.io/mdsr3e">2nd edition (plus light
edits and updates), published 2024</a></h3>
</div>
<div id="nd-edition-published-2021" class="section level3">
<h3><a href="https://mdsr-book.github.io/mdsr2e">2nd edition, published
2021</a></h3>
<p><em>Modern Data Science with R</em> is a comprehensive <strong>data
science textbook</strong> for undergraduates that incorporates
statistical and computational thinking to solve real-world problems with
data. Rather than focus exclusively on case studies or programming
syntax, this book illustrates how statistical programming in the
state-of-the-art R/RStudio computing environment can be leveraged to
extract meaningful information from a variety of data in the service of
addressing compelling statistical questions.</p>
<p>Contemporary data science requires a tight integration of knowledge
from statistics, computer science, mathematics, and a domain of
application. This book will help readers with some background in
statistics and modest prior experience with coding develop and practice
the appropriate skills to tackle complex data science projects. The book
features a number of exercises and has a flexible organization conducive
to teaching a variety of semester courses.</p>
</br>
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<p><a href="https://www.routledge.com/">
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<h3>1st edition, published 2017</h3>
<blockquote>
<p>“<em>Modern Data Science with R</em> is a breakthrough textbook” (<a
href="https://dl.acm.org/citation.cfm?id=3300155&dl=ACM&coll=DL">Allan
M. Miller</a>, UC Berkeley)</p>
</blockquote>
<blockquote>
<p>“<em>Modern Data Science with R</em>” is a landmark: the first full
textbook in data science…Indeed, if R were to cease to exist tomorrow,
these readers would still be well-situated to be data scientists. In a
nutshell, that approach is what makes this such a successful textbook
(not a handbook) suited for a course (not a workshop) on data science
(not statistics). (<a
href="https://amstat.tandfonline.com/doi/pdf/10.1080/00031305.2018.1496649">Andrew
Bray</a>, Reed College)</p>
</blockquote>
<blockquote>
<p>“The book is unique. It is an encyclopedia of Data Science, and it
covers a wide variety of modern topics; another positive aspect is that
it contains lots of examples and code, and the layout is quite catchy.
One can learn (and teach) subjects as diverse as: How to give talks,
administrating databases, how to model spatial data, and even ethics—all
in one book.” (Miguel de Carvalho, The University of Edinburgh)</p>
</blockquote>
<blockquote>
<p>“This book is unique because it incorporates theoretical fundamentals
such as statistical learning and regression modelling with the modern,
practical elements of data science, including setting up databases and
debugging. <em>Modern Data Science with R</em> presents a variety of
topics with several illustrative and engaging examples in R. This book
is a valuable resource to all those studying and interested in data
science.” (<a
href="https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12256">Shuangzhe
Liu</a>, University of Canberra)</p>
</blockquote>
<p><a href="publicity.html">Read more reviews…</a></p>
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