Ncss9SerialNumberBackground Exercise capacity is known to be an important prognostic factor in patients with cardiovascular disease, but it is uncertain whether it predicts mortality. The Popularity of Data Science Softwareby Robert A. Muenchen. Abstract. This article, formerly known as The Popularity of Data Analysis Software, 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. Software covered includes Actuate, Alpine, Alteryx, Angoss, Apache Flink, Apache Hive, Apache Mahout, Apache MXNet, Apache Pig, Apache Spark, BMDP, C, C or C, Caffe, Cognos, Data. Robot, Domino Data Labs, Enterprise Miner, FICO, FORTRAN, H2. O, Hadoop, Info Centricy or Xeno, Java, JMP, Julia, KNIME, Lavastorm, MATLAB, Megaputer or Poly. Analyst, Microsoft, Minitab, NCSS, Oracle Data Miner, Prognoz, Python, R, Rapid. Miner, Salford SPM, SAP, SAS, Scala, Spotfire, SPSS, SPSS Modeler, SQL, Stata, Statgraphics, Statistica, Systat, Tableau, Tensorflow, Teradata, Vowpal Wabbit, WEKAPentaho, and XGboost. Updates The most recent update was the Scholarly Articles section 61. 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 computerWhitworth, S. A., Berson, M. J. Computer technology in the social studies An examination of the effectiveness literature 19962001. Introduction. Please note that most of these Brand Names are registered Trade Marks, Company Names or otherwise controlled and their inclusion in this index is. 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 packagesDoes it fully support the style programming, or menus and dialog boxes, or workflow diagrams that you like Are its visualization options e. Does it provide output in the form you prefer e. La. Te. X integration Does it handle large enough data setsDo your colleagues use it so you can easily share data and programs Can you afford it 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. Lets examine each of them in turn. Job Advertisements. One of the best ways to measure the popularity or market share of software for data science 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., making its collection the best around. As their co founder and former CEO Paul Forster stated, Indeed. Monster, Careerbuilder, Hotjobs, Craigslist as well as hundreds of newspapers, associations, and company websites. Indeed. Searching for jobs using Indeed. Some software is used only for data science e. SPSS, Apache Spark while others are used in data science jobs and more broadly in report writing jobs e. SAS, Tableau. General purpose languages e. C, Java are heavily used in data science jobs, but the vast majority of jobs that use them have nothing to do with data science. To level the playing field I developed a protocol to focus the search for each software within only jobs for data scientists. The details of this protocol are described in a separate article, How to Search for Data Science Jobs. All of the graphs in this section use those procedures to make the required queries. I collected the job counts discussed in this section on February 2. One might think that a sample of on a single day might not be very stable, but the large number of job sources makes the counts in Indeed. The last time I collected this data was February 2. They grew between 7 and 1. Figure 1a shows that SQL is in the lead with nearly 1. Python and Java in the 1. Hadoop comes next with just over 1. R, the C variants, and SAS. The C, C, and C are combined in a single search since job advertisements usually seek any of them. This is the first time this report has shown more jobs for R than SAS, but keep in mind these are jobs specific to data science. If you open up the search to include jobs for report writing, youll find twice as many SAS jobs. Next comes Apache Spark, which was too new to be included in the 2. It has come a long way in an incredibly short time. For a detailed analysis of Sparks status, see Spark is the Future of Analytics, by Thomas Dinsmore. Tableau follows, with around 5,0. The 2. 01. 4 report excluded Tableau due to its jobs being dominated by report writing. Including report writing will quadruple the number of jobs for Tableau expertise to just over 2o,ooo. Figure 1a. The number of data science jobs for the more popular software those with 2. Apache Hive is next, with around 3,9. Scala, SAP, MATLAB, and SPSS, each having just over 2,5. After those, we see a slow decline from Teradata on down. Much of the software had fewer than 2. Convert Udf To Mp4 Software Player here. When displayed on the same graph as the industry leaders, their job counts appear to be zero therefore I have plotted them separately in Figure 1b. Alteryx comes out the leader of this group with 2. Microsoft was a difficult search since it appears in data science ads that mention other Microsoft products such as Windows or SQL Server. To eliminate such over counting, I treated Microsoft different from the rest by including product names such as Azure Machine Learning and Microsoft Cognitive Toolkit. So theres a good chance I went from over emphasizing Microsoft to under emphasizing it with only 1. Next comes the fascinating new high performance language Julia. I added FORTRAN just for fun and was surprised to see it still hanging in there after all these years. Apache Flink is also in this grouping, which all have around 1. H2. O follows, with just over 1. I find it fascinating that SAS Enterprise Miner, Rapid. Miner, and KNIME appear with a similar number of jobs around 9. Those three share a similar workflow user interface that make them particularly easy to use. The companies advertise the software as not needing much training, so it may be possible that companies feel little need to hire expertise if their existing staff picks it up more easily. SPSS Modeler also uses that type of interface, but its job count is about half that of the others, at 5. Bringing up the rear is Statistica, which was sold to Dell, then sold to Quest. Its 3. 6 jobs trails far behind its similar competitor, SPSS, which has a staggering 7. The open source MXNet deep learning framework, shows up next with 3. Tensorflow is a similar project with a 1. I expect both will be growing rapidly in the future. In the final batch that has few, if any, jobs, we see a few newcomers such as Data. Robot and Domino Data Labs. Others have been around for years, leaving us to wonder how they manage to stay afloat given all the competition. Its important to note that the values shown in Figures 1a and 1b are single points in time. The number of jobs for the more popular software do not change much from day to day. Therefore the relative rankings of the software shown in Figure 1a is unlikely to change much over the coming year.