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 The Popularity of Data Science Software in 2010, I followed only ten packages, all of them classic statistics software. The term data science hadn’t caught on yet, data mining was still a new thing. One of my recent blog posts covered 53 packages, and choosing them from a list of around 100 was a tough decision!

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 R-Bloggers.com or StatsBlogs.com, 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 Disruptive Analtyics, by Thomas Dinsmore.

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:

• Alluxio
• Alpine Data
• Alteryx
• APAMA
• Apex
• Arrow
• Caffe
• Cloudera
• Deeplearning4J
• Drill
• Flink
• Giraph
• Hadoop
• HAWQ
• Hive
• IBM SPSS Modeler
• Ignite
• Impala
• Kafka
• KNIME Analytics Platform
• Kylin
• MADLib
• Mahout
• MapR
• Microsoft R Aerver
• Phoenix
• Pig
• Python
• R
• RapidMiner
• Samza
• SAS
• SINGA
• Skytree Server
• Spark
• Storm
• Tajo
• Tensorflow
• Tez
• Theano
• Trafodion

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, ML/DL: Machine Learning, Deep Learning, extends the book’s coverage as data science software changes from week to week.

I highly recommend both the book and the blog. Have fun keeping up with the field!