![]() ![]() So, I divided the graph into three, each with a different scale. The distribution is so heterogeneous that placing them all on the same graph makes the values difficult to read. The number of jobs covers a very wide range, from zero to 164,996, with an average of 11,653.9 and an average of 845.0. I sometimes double check some calculations after a month or two and always get the same figures. Data collected in 20 using the same protocol are correlated r=.94, p=.002. One might think that a sample in a day may not be very stable, but they are. To measure the percentage change, I compare it with the data collected on May 27, 2019. I collected the job numbers on Octodiscussed in this section. All of the results in this section use those procedures to ask the necessary questions. 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 for data scientists to only focus on exploring each software within the jobs. General-purpose languages (eg, Python, C, Java) are used heavily in data science jobs, but are required by most jobs that have nothing to do with data science. Some software is used only for data science (eg, scikit-learn, Apache Spark), while others are used in data science jobs and, more broadly, in report-writing jobs (eg, SAS, Tableau) is done. Searching for jobs using is easy, but searching for software in a way that ensures fair comparison across packages is challenging. , association, and company websites.” also has great search capabilities. As their co-founder and former CEO Paul Forster put it, includes “all jobs from over 1,000 unique sources, including major job boards - Monster, CareerBuilder, HotJobs, Craigslist - as well as hundreds of newspapers. is America’s largest job site, besting its collection of job ads. ![]() Job demand change plots give us a good idea of what will be more popular in the future. ![]() Job ads are packed with information and backed by money, so they’re probably 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 the knowledge of each as a requirement. I repeat it here as a blog post, so you don’t have to read the whole article. This update covers perhaps the most important section, which measures popularity based on the number of job ads. I recently updated my comprehensive analysis of the popularity of data science software. , (You can report a problem about the content on this page here) Want to share your content on R-Blogger? Click here if you have a blog, or click here if you don’t. Data Science Software Popularity Update | r-bloggers ![]()
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February 2023
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