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The Popularity of Data Science Softwareby Robert A. Muenchen. Abstract. This article 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. Updates: The latest section on Growth in Scholarly Use was updated 6/8/2. 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 computer?
Comparison of the popularity or market share of data science, statistics, and advanced analytics software.
Does the software provide all the methods you need? If not, how extensible is it?
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Does its extensibility use its own unique language, or an external one (e. Python, R) that is commonly accessible from many packages? Does it fully support the style (programming, or menus and dialog boxes, or workflow diagrams) that you like? Are its visualization options (e.
La. Te. X integration)? Does it handle large enough data sets? Do your colleagues use it so you can easily share data and programs? Can you afford it?
The software I track currently includes: Alpine, Alteryx, Angoss, C / C++ / C#, BMDP, IBM SPSS Statistics, IBM SPSS Modeler, Info. Centricity Xeno, Java, JMP, KNIME, Lavastorm, Mathworks. Revolution R Enterprise or TIBCO Enterprise Runtime for R; or SAS vs. Cognos), or are tied to a specific database (e.
Microsoft, Oracle, SAP), specific hardware (e. Teradata, IBM Pure. Data) or a specific application field. I also exclude packages devoted more to visualization, such as Tableau, Spotfire, Origin, and Sigma. Plot. These packages do occasionally appear in plots borrowed from other sites. 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.
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SPSS Links, Online SPSS Tutorials,. At this Site; SPSS Statistics General Guides; Specialized SPSS Tutorials or Technical Papers; At this Site Weighting /static/tutorials/WEIGHTING.pdf. ANALISIS ESTADISTICO CON EL SPSS Ramiro Ra The following video tutorials are currently available. We upload videos periodically, so please check back to view new videos. 121 Tutorial #1: Using Latent GOLD choice to Estimate Discrete Choice Models In this tutorial, we analyze data from a simple choice-based conjoint (CBC) experiment designed to estimate market shares (choice shares) for shoes. Quantitative Data Analysis Using SPSS 15. Jean Russell Bob Booth November 2008 AP-SPSS2 University of Sheffield.
Competition Use. Growth in Capability. Let. 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. As their CEO and co- founder Paul Forster stated, Indeed. For a package that has a unique name, all that is required is a simple search on that name.
However, for software that. R) or that is general purpose (e. Java) it required complex searches and/or some rather tricky calculations which are described in the companion article, How to Search for Data Science Jobs. All of the graphs in this section use those procedures to make the required queries. Figure 1a shows that Java is in the lead followed by SAS.
Python or C, C++/C# are roughly tied for third place. The tie between C and Python is not surprising as many advertisements for analytics jobs that use programming mention both together. The number of analytics jobs for the more popular software (2. R resides in an interestingly large gap between the other domain- specific languages, SAS and SPSS. R has not only caught up with SPSS, but surpassed it with around 5. MATLAB has many similarities to R, so it. Note that these are specific to analtyics and MATLAB has many engineering jobs that are not counted in this total.
Much of the software had fewer than 2. When displayed on the same graph as the industry leaders, their job counts appeared to be zero.
Therefore I have plotted them separately in Figure 1b. FICO comes out the leader of this group, followed by Enterprise Miner. Statistica and Alteryx are close to tied at around 5. From Rapid. Miner on down, the decline in jobs is fairly smooth. The number of analytics jobs for the less popular software (under 2.
It. 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 likely to shift from month to month. Each software has an overall trend that shows how the demand for jobs changes across the years. You can plot these trends using Indeed. However, as before, focusing just on analytics jobs requires carefully constructed queries, and when comparing two trends at a time means they both have to fit in the same query limit allowed by Indeed. Those details are described here.
I. Figure 1c compares the number of analytics jobs available for R and SPSS across time. Analytics jobs for SPSS have not changed much over the years, while those for R have been steadily increasing. The jobs for R finally crossed over and exceeded those for SPSS toward the middle of 2. We know from Figure 1a that SAS is still far ahead of R in analytics job postings. How far does R have to go to catch up with SAS?
Figure 1d provides one perspective. It would be nice to have the data to forecast when R. However, we can use the approximate slope of each line to get a rough estimate.
If jobs for SAS stay level and those for R continue to grow linearly as they have since January 2. R will catch up in 3. If instead the demand for SAS jobs that started in January of 2.
R will catch up in 1. A debate has been taking place on the Internet regarding the relative place of Python and R. Ironically, this debate about software to do data analytics has involved very little actual data.
However it is possible now to at least study the job trends. Figure 1a showed us that Python is well out in front of R, at least on that single day the searches were run. What has the data looked like over time? The answer is shown in Figure 1e. Note that in this graph, Python appears to have less of advantage in Figure 1e than it had in Figure 1a.
The final point on the trend graph was done only a few days after the queries used in Figure 1a, and that data changed very little in the meantime. The difference is due to the fact that Indeed. Here is the query used for Figure 1e, and the analytic terms it contains were fewer than the one used for Figure 1a. R. and (. I only include it here because the IT advisory firm Gartner, Inc. The more popular a software package is, the more likely it will appear in scholarly publications as an analysis tool or even an object of study. The software that is used in scholarly articles is what the next generation of analysts will graduate knowing, so it.
Google Scholar offers a way to measure such activity. However, no search of this magnitude is perfect; each will include some irrelevant articles and reject some relevant ones. The details of the search terms I used are complex enough to move to a companion article, How to Search For Data Science Articles. Since Google regularly improves its search algorithm, each year I re- collect the data for all years. Figure 2a shows the number of articles found for each software package for 2. SPSS is by far the most dominant package, as it has been for over 1. This may be due to its balance between power and ease- of- use.
For the first time ever, R is in second place with around half as many articles. Although now in third place, SAS is nearly tied with R. Stata and MATLAB are essentially tied for fourth and fifth place. Starting with Java, usage slowly tapers off. Note that the general- purpose software C, C++, C#, MATLAB, Java, and Python are included only when found in combination with data science terms, so view those as much rougher counts than the rest. Since Scala and Julia have a heavy data science angle to them, I cut them some slack by not filtering the search by adding data science terms.
So any articles that used Scala or Julia were included in the total count for these languages regardless of usage, not that it helped them much! Figure 2a. Number of scholarly articles found in the most recent complete year (2. From Spark on down, the counts appear to be zero, but that. The counts are just very low compared to the more popular packages, used in tens of thousands articles.
Figure 2b shows the software only for those packages that have fewer than 1,2. Spark and Rapid. Miner top out the list of these packages, followed by KNIME and BMDP.
Then comes a group of mostly relative new arrivals beginning with Microsoft. However, this group includes Megaputer, whose Polyanalyst software has been around for many years now, with little progress to show for it. Dead last is Lavastorm, which to my knowledge is the only commercial package that includes Tibco. The number of scholarly articles for software that was used by fewer than 1,2. Figures 2a and 2b are useful for studying market share as it is now, but they don.
It would be ideal to have long- term growth trend graphs for each of the analytics packages, but collecting such data is too time consuming since it must be re- collected every year. This provides the data we need to study year- over- year changes. Figure 2c shows the percent change across those years, with the . Those whose use is declining or . Since the number of articles tends to be in the thousands or tens of thousands, I have removed any software that had fewer than 5.
Figure 2c. Change in the number of scholarly articles using each software in the most recent two complete years (2. Packages shown in red are . Note that the Python figures are strictly for data science use as defined here. The open- source KNIME and Rapid. Miner are the second and third fastest growing, respectively. Both use the easy yet powerful workflow approach to data science. Figure 2b shows that Rapid.
Miner has almost twice the marketshare of KNIME, but here we see use of KNIME is growing faster. That may be due to KNIME. The companies are two of only four chosen by IT advisory firm Gartner, Inc.