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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.
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. 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 popular science articles to write about 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 24, 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 20,and those that were collected using the same protocol the general purpose languages yielded quite similar results. 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.
Next comes Apache Spark, which was too new to be included in the report.
It has come a long way in an incredibly short time. Tableau follows, with around 5, jobs.
The 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. After those, we see a slow decline from Teradata on down. Much of the software had fewer than job listings.
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 jobs.
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.
Next comes the fascinating new high-performance language Julia. Apache Flink is also in this grouping, which all have around jobs. H2O follows, with just over jobs. 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 50 jobs. Bringing up the rear is Statistica, which was sold to Dell, then sold to Quest.
Its 36 jobs trails far behind its similar competitor, SPSS, which has a staggering fold job advantage. The open source MXNet deep learning framework, shows up next with 34 jobs. Tensorflow is a similar project with a fold job advantage, but these two are both young enough that 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 DataRobot and Domino Data Labs. Others have been around for years, leaving us to wonder how they manage to stay afloat given all the competition.
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. The less popular packages shown in Figure 1b have such low job counts that their ranking is more likely to shift from month to month, though their position relative to the major packages should remain more stable.
Each software has an overall trend that shows how the demand for jobs changes across the years.by 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 benjaminpohle.com software is also referred to as tools for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, business analytics, and is also.
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