Plots of change in job demand give us a good idea of what is likely to become more popular in the future.
Job ads are rich in information and are backed by money, so they are perhaps the best measure of how popular each software is now. 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 that highlight knowledge of each as a requirement. In rough order of the quality of the data, these include: There are many ways to measure popularity or market share and each has its advantages and disadvantages.
I announce the updates to this article on Twitter: Introduction Updates: The most recent updates were to the Job Advertisement section on Scholarly Articles section on IT Research Firms section on Forrester Research Inc. Software covered includes:Īctuate, Alpine, Alteryx, Angoss, Apache Flink, Apache Hive, Apache Mahout, Apache MXNet, Apache Pig, Apache Spark, BMDP, C, C++ or C#, Caffe, Cognos, DataRobot, Domino Data Labs, Enterprise Miner, FICO, FORTRAN, H2O, Hadoop, Info Centricy or Xeno, Java, JMP, Julia, KNIME, Lavastorm, MATLAB, Megaputer or PolyAnalyst, Microsoft, Minitab, NCSS, Oracle Data Miner, Prognoz, Python, R, RapidMiner, Salford SPM, SAP, SAS, Scala, Spotfire, SPSS, SPSS Modeler, SQL, Stata, Statgraphics, Statistica, Systat, Tableau, Tensorflow, Teradata, Vowpal Wabbit, WEKA/Pentaho, and XGboost. 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. 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.