Finally, we see a set slowly declining from MATLAB at 17,736 to Scala at 11,473.įigure 1a. Next comes R and SAS, both around 24K jobs, with R slightly in the lead. Tableau, one of Power BI’s major competitors, is in that set. Next comes a set from C++/C# at 48,555, slowly declining to Microsoft’s Power BI at 38,125. ![]() SQL is in the lead with 164,996 jobs, followed by Python with 150,992 and Java with 113,944. ![]() A single plot with a logarithmic scale would be an alternative, but when I asked some mathematically astute people how various packages compared on such a plot, they were so far off that I dropped that approach.įigure 1a shows the most popular tools, those with at least 10,000 jobs. Therefore, I split the graph into three, each with a different scale. The distribution is so skewed that placing them all on the same graph makes reading values difficult. The number of jobs covers a very wide range from zero to 164,996, with a mean of 11,653.9 and a median of 845.0. I occasionally double-check some counts a month or so later and always get similar figures. Data collected in 20 using the same protocol correlated r=.94, p=.002. One might think that a sample on a single day might not be very stable, but they are. To measure percent change, I compare that to data collected on May 27, 2019. I collected the job counts discussed in this section on October 5, 2022. All of the results in this section use those procedures to make the required queries. The details of this protocol are described in a separate article, How to Search for Data Science Jobs. To level the playing field, I developed a protocol to focus the search for each software within only jobs for data scientists. General-purpose languages (e.g., Python, C, Java) are heavily used in data science jobs, but the vast majority of jobs that require them have nothing to do with data science. Some software is used only for data science (e.g., scikit-learn, Apache Spark), while others are used in data science jobs and, more broadly, in report-writing jobs (e.g., SAS, Tableau). Searching for jobs using is easy, but searching for software in a way that ensures fair comparisons across packages is challenging. As their co-founder and former CEO Paul Forster stated, includes “all the jobs from over 1,000 unique sources, comprising the major job boards – Monster, CareerBuilder, HotJobs, Craigslist – as well as hundreds of newspapers, associations, and company websites.” also has superb search capabilities. is the biggest job site in the U.S., making its collection of job ads the best around. Plots of change in job demand give us a good idea of what will 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. Do your colleagues use it so you can easily share data and programs?.Does it provide output in the form you prefer (e.g., cut & paste into a word processor vs.Are its visualization options (e.g., static vs.Does it fully support the style (programming, menus and dialog boxes, or workflow diagrams) that you like?.Python, R) that is commonly accessible from many packages? Does its extensibility use its own unique language or an external one (e.g.Does the software provide all the methods you need? If not, how extensible is it?. ![]() When choosing a tool for data analysis, now more commonly referred to as analytics or data science, there are many factors to consider: I announce the updates to this article on Twitter: Introduction Updates: The most recent update was to the Job Advertisement section on. Such software is also referred to as tools for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, and 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.
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