Comparison of the popularity or market share of data science, statistics, and advanced analytics software. Management Systems International (MSI), a Tetra Tech company, is a US-based international development firm that specializes in designing, implementing and evaluating. Online statistics courses & certificates in data science, biostatistics, data mining, text analytics, introductory statistics, Bayesian, spatial statistics. PANTONE PLUS SERIES for Graphics and Multimedia. More ways to Make it Brilliant.
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The Popularity of Data Science Softwareby Robert A. Muenchen. Abstract. This article presents various ways of measuring the popularity or market share of software for advanced analytics software. Such software is also referred to as tools for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, business analytics, and is also a subset of business intelligence. Updates: The latest section on Growth in Scholarly Use was updated 6/8/2. I announce the updates to this article on Twitter: http: //twitter. Bob. Muenchen. Introduction.
When choosing a tool for data analysis, now more commonly referred to as analytics or data science, there are many factors to consider: Does it run natively on your computer? Does the software provide all the methods you need? If not, how extensible is it? Does its extensibility use its own unique language, or an external one (e. Python, R) that is commonly accessible from many packages? Does it fully support the style (programming, or menus and dialog boxes, or workflow diagrams) that you like? Are its visualization options (e.
La. Te. X integration)? Does it handle large enough data sets? Do your colleagues use it so you can easily share data and programs? Can you afford it?
The software I track currently includes: Alpine, Alteryx, Angoss, C / C++ / C#, BMDP, IBM SPSS Statistics, IBM SPSS Modeler, Info. Centricity Xeno, Java, JMP, KNIME, Lavastorm, Mathworks’ MATLAB, Megaputer’s Poly. Analyst, Minitab, NCSS, Python, R, Rapid. Miner, SAS, SAS Enterprise Miner, Salford Predictive Modeler (SPM) etc., SAP’S KXEN, Stata, Statistica, Systat, WEKA / Pentaho. I don. Revolution R Enterprise or TIBCO Enterprise Runtime for R; or SAS vs. Cognos), or are tied to a specific database (e. Microsoft, Oracle, SAP), specific hardware (e.
Teradata, IBM Pure. Wireless Driver For Linux Mint 15. Data) or a specific application field. I also exclude packages devoted more to visualization, such as Tableau, Spotfire, Origin, and Sigma. Plot. These packages do occasionally appear in plots borrowed from other sites.
There are many ways to measure popularity or market share and each has its advantages and disadvantages. In rough order of the quality of the data, these include: Job Advertisements.
Scholarly Articles. IT Research Firm Reports. Surveys of Use. Books.
Blogs. Discussion Forum Activity. Programming Popularity Measures. Sales & Downloads. Competition Use. Growth in Capability. Let’s examine each of them in turn. Job Advertisements. One of the best ways to measure the popularity or market share of software for analytics is to count the number of job advertisements for each.
Job advertisements are rich in information and are backed by money so they are perhaps the best measure of how popular each software is now. Plots of job trends give us a good idea of what is likely to become more popular in the future. Indeed. com is the biggest job site in the U. S. As their CEO and co- founder Paul Forster stated, Indeed. For a package that has a unique name, all that is required is a simple search on that name.
However, for software that’s hard to locate (e. R) or that is general purpose (e. Java) it required complex searches and/or some rather tricky calculations which are described in the companion article, How to Search for Data Science Jobs.
All of the graphs in this section use those procedures to make the required queries. Figure 1a shows that Java is in the lead followed by SAS. Python or C, C++/C# are roughly tied for third place. The tie between C and Python is not surprising as many advertisements for analytics jobs that use programming mention both together. The number of analytics jobs for the more popular software (2. R resides in an interestingly large gap between the other domain- specific languages, SAS and SPSS.
R has not only caught up with SPSS, but surpassed it with around 5. MATLAB has many similarities to R, so it’s interesting to see that it has only around half the job postings.
Note that these are specific to analtyics and MATLAB has many engineering jobs that are not counted in this total. Much of the software had fewer than 2. When displayed on the same graph as the industry leaders, their job counts appeared to be zero. Therefore I have plotted them separately in Figure 1b.
FICO comes out the leader of this group, followed by Enterprise Miner. Statistica and Alteryx are close to tied at around 5. From Rapid. Miner on down, the decline in jobs is fairly smooth. Megaputer’s Polyanalyst job count is actually zero.
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