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(This article was first published on <strong><a href="http://r4stats.com/2017/04/12/keeping-up-with-your-data-science-options/"> R – r4stats.com</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>The field of data science is changing so rapidly that it’s quite hard to keep up with it all. When I first started tracking <a target="_blank" rel="nofollow" href="http://r4stats.com/articles/popularity/">The Popularity of Data Science Software</a> in 2010, I followed only ten packages, all of them classic statistics software. The term <em>data science</em> hadn’t caught on yet, <em>data mining</em> was still a new thing. One of my recent <a target="_blank" rel="nofollow" href="http://r4stats.com/2017/02/28/r-passes-sas/">blog posts</a> covered 53 packages, and choosing them from a list of around 100 was a tough decision!</p>
<p>To keep up with the rapidly changing field, you can read the information on a package’s web site, see what people are saying on blog aggregators such as <a target="_blank" rel="nofollow" href="https://www.r-bloggers.com/">R-Bloggers.com</a> or <a target="_blank" rel="nofollow" href="http://www.statsblogs.com/">StatsBlogs.com</a>, and if it sounds good, download a copy and try it out. What’s much harder to do is figure out how they all relate to one another. A helpful source of information on that front is the book <a target="_blank" rel="nofollow" href="http://amzn.to/2oncmFG">Disruptive Analtyics</a>, by <a target="_blank" rel="nofollow" href="https://www.linkedin.com/in/thomaswdinsmore/">Thomas Dinsmore</a>.</p>
<p><a target="_blank" rel="nofollow" href="https://i1.wp.com/r4stats.com/wp-content/uploads/2017/04/DisruptiveAnalytics.png"><img sizes="(max-width: 389px) 100vw, 389px" data-image-description="" data-orig-file="https://i1.wp.com/r4stats.com/wp-content/uploads/2017/04/DisruptiveAnalytics.png?fit=389%2C590" data-orig-size="389,590" data-image-title="DisruptiveAnalytics" class="wp-image-2166" data-comments-opened="1" data-recalc-dims="1" data-attachment-id="2166" data-permalink="http://r4stats.com/2017/04/12/keeping-up-with-your-data-science-options/disruptiveanalytics/" alt="" data-medium-file="https://i1.wp.com/r4stats.com/wp-content/uploads/2017/04/DisruptiveAnalytics.png?fit=198%2C300" data-large-file="https://i1.wp.com/r4stats.com/wp-content/uploads/2017/04/DisruptiveAnalytics.png?fit=389%2C590" data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}" src="https://i1.wp.com/r4stats.com/wp-content/uploads/2017/04/DisruptiveAnalytics.png?resize=389%2C590"></a></p>
<p>I was lucky enough to be the technical reviewer for the book, during which time I ended up reading it twice. I still refer to it regularly as it covers quite a lot of material. In a mere 262 pages, Dinsmore manages to describe each of the following packages, how they relate to one another, and how they fit into the big picture of data science:</p>
<ul>
<li>Alluxio</li>
<li>Alpine Data</li>
<li>Alteryx</li>
<li>APAMA</li>
<li>Apex</li>
<li>Arrow</li>
<li>Caffe</li>
<li>Cloudera</li>
<li>Deeplearning4J</li>
<li>Drill</li>
<li>Flink</li>
<li>Giraph</li>
<li>Hadoop</li>
<li>HAWQ</li>
<li>Hive</li>
<li>IBM SPSS Modeler</li>
<li>Ignite</li>
<li>Impala</li>
<li>Kafka</li>
<li>KNIME Analytics Platform</li>
<li>Kylin</li>
<li>MADLib</li>
<li>Mahout</li>
<li>MapR</li>
<li>Microsoft R Aerver</li>
<li>Phoenix</li>
<li>Pig</li>
<li>Python</li>
<li>R</li>
<li>RapidMiner</li>
<li>Samza</li>
<li>SAS</li>
<li>SINGA</li>
<li>Skytree Server</li>
<li>Spark</li>
<li>Storm</li>
<li>Tajo</li>
<li>Tensorflow</li>
<li>Tez</li>
<li>Theano</li>
<li>Trafodion</li>
</ul>
<p>As you can tell from the title, a major theme of the book is how open source software is disrupting the data science marketplace. Dinsmore’s blog, <a target="_blank" rel="nofollow" href="https://thomaswdinsmore.com/">ML/DL</a>: Machine Learning, Deep Learning, extends the book’s coverage as data science software changes from week to week.</p>
<p>I highly recommend both the book and the blog. Have fun keeping up with the field!</p>
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