Tag: Data Science

Update to Data Science Software Popularity

I’ve updated The Popularity of Data Science Software‘s market share estimates based on scholarly articles. I posted it below, so you don’t have to sift through the main article to read the new section.

Scholarly Articles

Scholarly articles provide a rich source of information about data science tools. Because publishing requires significant effort, analyzing the type of data science tools used in scholarly articles provides a better picture of their popularity than a simple survey of tool usage. The more popular a software package is, the more likely it will appear in scholarly publications as an analysis tool or even as an object of study.

Since scholarly articles tend to use cutting-edge methods, the software used in them can be a leading indicator of where the overall market of data science software is headed. 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.  

Figure 2a shows the number of articles found for the more popular software packages and languages (those with at least 4,500 articles) in the most recent complete year, 2022.

Figure 2a. The number of scholarly articles found on Google Scholar for data science software. Only those with more than 4,500 citations are shown.

SPSS is the most popular package, as it has been for over 20 years. This may be due to its balance between power and its graphical user interface’s (GUI) ease of use. R is in second place with around two-thirds as many articles. It offers extreme power, but as with all languages, it requires memorizing and typing code. GraphPad Prism, another GUI-driven package, is in third place. The packages from MATLAB through TensorFlow are roughly at the same level. Next comes Python and Scikit Learn. The latter is a library for Python, so there is likely much overlap between those two. Note that the general-purpose languages: C, C++, C#, FORTRAN, Java, MATLAB, and Python are included only when found in combination with data science terms, so view those counts as more of an approximation than the rest. Old stalwart FORTRAN appears last in this plot. While its count seems close to zero, that’s due to the wide range of this scale, and its count is just over the 4,500-article cutoff for this plot.

Continuing on this scale would make the remaining packages appear too close to the y-axis to read, so Figure 2b shows the remaining software on a much smaller scale, with the y-axis going to only 4,500 rather than the 110,000 used in Figure 2a. I chose that cutoff value because it allows us to see two related sets of tools on the same plot: workflow tools and GUIs for the R language that make it work much like SPSS.

Figure 2b. Number of scholarly articles using each data science software found using Google Scholar. Only those with fewer than 4,500 citations are shown.

JASP and jamovi are both front-ends to the R language and are way out front in this category. The next R GUI is R Commander, with half as many citations. Still, that’s far more than the rest of the R GUIs: BlueSky Statistics, Rattle, RKWard, R-Instat, and R AnalyticFlow. While many of these have low counts, we’ll soon see that the use of nearly all is rapidly growing.

Workflow tools are controlled by drawing 2-dimensional flowcharts that direct the flow of data and models through the analysis process. That approach is slightly more complex to learn than SPSS’ simple menus and dialog boxes, but it gets closer to the complete flexibility of code. In order of citation count, these include RapidMiner, KNIME, Orange Data Mining, IBM SPSS Modeler, SAS Enterprise Miner, Alteryx, and R AnalyticFlow. From RapidMiner to KNIME, to SPSS Modeler, the citation rate approximately cuts in half each time. Orange Data Mining comes next, at around 30% less. KNIME, Orange, and R Analytic Flow are all free and open-source.

While Figures 2a and 2b help study market share now, they don’t show how things are changing. It would be ideal to have long-term growth trend graphs for each software, but collecting that much data is too time-consuming. Instead, I’ve collected data only for the years 2019 and 2022. This provides the data needed to study growth over that period.

Figure 2c shows the percent change across those years, with the growing “hot” packages shown in red (right side) and the declining or “cooling” ones shown in blue (left side).

Figure 2c. Change in Google Scholar citation rate from 2019 to the most recent complete year, 2022. BlueSky (2,960%) and jamovi (452%) growth figures were shrunk to make the plot more legible.

Seven of the 14 fastest-growing packages are GUI front-ends that make R easy to use. BlueSky’s actual percent growth was 2,960%, which I recoded as 220% as the original value made the rest of the plot unreadable. In 2022 the company released a Mac version, and the Mayo Clinic announced its migration from JMP to BlueSky; both likely had an impact. Similarly, jamovi’s actual growth was 452%, which I recoded to 200. One of the reasons the R GUIs were able to obtain such high percentages of change is that they were all starting from low numbers compared to most of the other software. So be sure to look at the raw counts in Figure 2b to see the raw counts for all the R GUIs.

The most impressive point on this plot is the one for PyTorch. Back on 2a we see that PyTorch was the fifth most popular tool for data science. Here we see it’s also the third fastest growing. Being big and growing fast is quite an achievement!

Of the workflow-based tools, Orange Data Mining is growing the fastest. There is a good chance that the next time I collect this data Orange will surpass SPSS Modeler.

The big losers in Figure 2c are the expensive proprietary tools: SPSS, GraphPad Prism, SAS, BMDP, Stata, Statistica, and Systat. However, open-source R is also declining, perhaps a victim of Python’s rising popularity.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2d, I have plotted the same scholarly-use data for 1995 through 2016.

Figure 2d. The number of Google Scholar citations for each classic statistics package per year from 1995 through 2016.

SPSS has a clear lead overall, but now you can see that its dominance peaked in 2009, and its use is in sharp decline. SAS never came close to SPSS’s level of dominance, and its usage peaked around 2010. GraphPad Prism followed a similar pattern, though it peaked a bit later, around 2013.

In Figure 2d, the extreme dominance of SPSS makes it hard to see long-term trends in the other software. To address this problem, I have removed SPSS and all the data from SAS except for 2014 and 2015. The result is shown in Figure 2e.

Figure 2e. The number of Google Scholar citations for each classic statistics package from 1995 through 2016, with SPSS removed and SAS included only in 2014 and 2015. The removal of SPSS and SAS expanded scale makes it easier to see the rapid growth of the less popular packages.

Figure 2e shows that most of the remaining packages grew steadily across the time period shown. R and Stata grew especially fast, as did Prism until 2012. The decline in the number of articles that used SPSS, SAS, or Prism is not balanced by the increase in the other software shown in this graph.

These results apply to scholarly articles in general. The results in specific fields or journals are likely to differ.

You can read the entire Popularity of Data Science Software here; the above discussion is just one section.

Updated Comparison of R Graphical User Interfaces

I have just updated my detailed reviews of Graphical User Interfaces (GUIs) for R, so let’s compare them again. It’s not too difficult to rank them based on the number of features they offer, so let’s start there. I’m basing the counts on the number of dialog boxes in each category of four categories:

  • Ease of Use
  • General Usability
  • Graphics
  • Analytics

This is trickier data to collect than you might think. Some software has fewer menu choices, depending instead on more detailed dialog boxes. Studying every menu and dialog box is very time-consuming, but that is what I’ve tried to do. I’m putting the details of each measure in the appendix so you can adjust the figures and create your own categories. If you decide to make your own graphs, I’d love to hear from you in the comments below.

Figure 1 shows how the various GUIs compare on the average rank of the four categories. R Commander is abbreviated Rcmdr, and R AnalyticFlow is abbreviated RAF. We see that BlueSky is in the lead with R-Instat close behind. As my detailed reviews of those two point out, they are extremely different pieces of software! Rather than spend more time on this summary plot, let’s examine the four categories separately.

Figure 1. Mean of each R GUI’s ranking of the four categories. To make this plot consistent with the others below, the larger the rank, the better.

For the category of ease-of-use, I’ve defined it mostly by how well each GUI does what GUI users are looking for: avoiding code. They get one point each for being able to install, start, and use the GUI to its maximum effect, including publication-quality output, without knowing anything about the R language itself. Figure two shows the result. JASP comes out on top here, with jamovi and BlueSky right behind.

Figure 2. The number of ease-of-use features that each GUI has.

Figure 3 shows the general usability features each GUI offers. This category is dominated by data-wrangling capabilities, where data scientists and statisticians spend most of their time. This category also includes various types of data input and output. BlueSky and R-Instat come out on top not just due to their excellent selection of data wrangling features but also due to their use of the rio package for importing and exporting files. The rio package combines the import/export capabilities of many other packages, and it is easy to use. I expect the other GUIs will eventually adopt it, raising their scores by around 40 points. JASP shows up at the bottom of this plot due to its philosophy of encouraging users to prepare the data elsewhere before importing it into JASP.

Figure 3. Number of general usability features for each GUI.

Figure 4 shows the number of graphics features offered by each GUI. R-Instat has a solid lead in this category. In fact, this underestimates R-Instat’s ability if you…

Continued…

Gartner’s 2019 Take on Data Science Software

I’ve just updated The Popularity of Data Science Software to reflect my take on Gartner’s 2019 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging through all 40+ pages of my report, here’s just the updated section:

IT Research Firms

IT research firms study software products and corporate strategies. They survey customers regarding their satisfaction with the products and services and provide their analysis in reports that they sell to their clients. Each research firm has its own criteria for rating companies, so they don’t always agree. However, I find the detailed analysis that these reports contain extremely interesting reading. The reports exclude open source software that has no specific company backing, such as R, Python, or jamovi. Even open source projects that do have company backing, such as BlueSky Statistics, are excluded if they have yet to achieve sufficient market adoption. However, they do cover how company products integrate open source software into their proprietary ones.

While these reports are expensive, the companies that receive good ratings usually purchase copies to give away to potential customers. An Internet search of the report title will often reveal companies that are distributing them. On the date of this post, Datarobot is offering free copies.

Gartner, Inc. is one of the research firms that write such reports.  Out of the roughly 100 companies selling data science software, Gartner selected 17 which offered “cohesive software.” That software performs a wide range of tasks including data importation, preparation, exploration, visualization, modeling, and deployment.

Gartner analysts rated the companies on their “completeness of vision” and their “ability to execute” that vision. Figure 3a shows the resulting “Magic Quadrant” plot for 2019, and 3b shows the plot for the previous year. Here I provide some commentary on their choices, briefly summarize their take, and compare this year’s report to last year’s. The main reports from both years contain far more detail than I cover here.

Gartner-2019

Figure 3a. Gartner Magic Quadrant for Data Science and Machine Learning Platforms from their 2019 report (plot done in November 2018, report released in 2019).

The Leaders quadrant is the place for companies whose vision is aligned with their customer’s needs and who have the resources to execute that vision. The further toward the upper-right corner of the plot, the better the combined score.

  • RapidMiner and KNIME reside in the best part of the Leaders quadrant this year and last. This year RapidMiner has the edge in ability to execute, while KNIME offers more vision. Both offer free and open source versions, but the companies differ quite a lot on how committed they are to the open source concept. KNIME’s desktop version is free and open source and the company says it will always be so. On the other hand, RapidMiner is limited by a cap on the amount of data that it can analyze (10,000 cases) and as they add new features, they usually come only via a commercial license with “difficult-to-navigate pricing conditions.” These two offer very similar workflow-style user interfaces and have the ability to integrate many open sources tools into their workflows, including R, Python, Spark, and H2O.
  • Tibco moved from the Challengers quadrant last year to the Leaders this year. This is due to a number of factors, including the successful integration of all the tools they’ve purchased over the years, including Jaspersoft, Spotfire, Alpine Data, Streambase Systems, and Statistica.
  • SAS declined from being solidly in the Leaders quadrant last year to barely being in it this year. This is due to a substantial decline in its ability to execute. Given SAS Institute’s billions in revenue, that certainly can’t be a financial limitation. It may be due to SAS’ more limited ability to integrate as wide a range of tools as other vendors have. The SAS language itself continues to be an important research tool among those doing complex mixed-effects linear models. Those models are among the very few that R often fails to solve.

The companies in the Visionaries Quadrant are those that have good future plans but which may not have the resources to execute that vision.

  • Mathworks moved forward substantially in this quadrant due to MATLAB’s ability to handle unconventional data sources such as images, video, and the Internet of Things (IoT). It has also opened up more to open source deep learning projects.
  • H2O.ai is also in the Visionaries quadrant. This is the company behind the open source  H2O software, which is callable from many other packages or languages including R, Python, KNIME, and RapidMiner. While its own menu-based interface is primitive, its integration into KNIME and RapidMiner makes it easy to use for non-coders. H2O’s strength is in modeling but it is lacking in data access and preparation, as well as model management.
  • IBM dropped from the top of the Visionaries quadrant last year to the middle. The company has yet to fully integrate SPSS Statistics and SPSS Modeler into its Watson Studio. IBM has also had trouble getting Watson to deliver on its promises.
  • Databricks improved both its vision and its ability to execute, but not enough to move out of the Visionaries quadrant. It has done well with its integration of open-source tools into its Apache Spark-based system. However, it scored poorly in the predictability of costs.
  • Datarobot is new to the Gartner report this year. As its name indicates, its strength is in the automation of machine learning, which broadens its potential user base. The company’s policy of assigning a data scientist to each new client gets them up and running quickly.
  • Google’s position could be clarified by adding more dimensions to the plot. Its complex collection of a dozen products that work together is clearly aimed at software developers rather than data scientists or casual users. Simply figuring out what they all do and how they work together is a non-trivial task. In addition, the complete set runs only on Google’s cloud platform. Performance on big data is its forte, especially problems involving image or speech analysis/translation.
  • Microsoft offers several products, but only its cloud-only Azure Machine Learning (AML) was comprehensive enough to meet Gartner’s inclusion criteria. Gartner gives it high marks for ease-of-use, scalability, and strong partnerships. However, it is weak in automated modeling and AML’s relation to various other Microsoft components is overwhelming (same problem as Google’s toolset).

Figure 3b. Last year’s Gartner Magic Quadrant for Data Science and Machine Learning Platforms (January, 2018)

Those in the Challenger’s Quadrant have ample resources but less customer confidence in their future plans, or vision.

  • Alteryx dropped slightly in vision from last year, just enough to drop it out of the Leaders quadrant. Its workflow-based user interface is very similar to that of KNIME and RapidMiner, and it too gets top marks in ease-of-use. It also offers very strong data management capabilities, especially those that involve geographic data, spatial modeling, and mapping. It comes with geo-coded datasets, saving its customers from having to buy it elsewhere and figuring out how to import it. However, it has fallen behind in cutting edge modeling methods such as deep learning, auto-modeling, and the Internet of Things.
  • Dataiku strengthed its ability to execute significantly from last year. It added better scalability to its ease-of-use and teamwork collaboration. However, it is also perceived as expensive with a “cumbersome pricing structure.”

Members of the Niche Players quadrant offer tools that are not as broadly applicable. These include Anaconda, Datawatch (includes the former Angoss), Domino, and SAP.

  • Anaconda provides a useful distribution of Python and various data science libraries. They provide support and model management tools. The vast army of Python developers is its strength, but lack of stability in such a rapidly improving world can be frustrating to production-oriented organizations. This is a tool exclusively for experts in both programming and data science.
  • Datawatch offers the tools it acquired recently by purchasing Angoss, and its set of “Knowledge” tools continues to get high marks on ease-of-use and customer support. However, it’s weak in advanced methods and has yet to integrate the data management tools that Datawatch had before buying Angoss.
  • Domino Data Labs offers tools aimed only at expert programmers and data scientists. It gets high marks for openness and ability to integrate open source and proprietary tools, but low marks for data access and prep, integrating models into day-to-day operations, and customer support.
  • SAP’s machine learning tools integrate into its main SAP Enterprise Resource Planning system, but its fragmented toolset is weak, and its customer satisfaction ratings are low.

To see many other ways to rate this type of software, see my ongoing article, The Popularity of Data Science Software. You may also be interested in my in-depth reviews of point-and-click user interfaces to R. I invite you to subscribe to my blog or follow me on twitter where I announce new posts. Happy computing!

Business Intelligence and Data Science Groups in East Tennessee

The Knoxville area has four groups that help people learn about business intelligence and data science.

The Knoxville R Users Group (KRUG) focuses on the free and open source R language. Each meeting begins with a bit of socializing followed by a series of talks given by its members or guests. The talks range from brief five-minute demos of an R function to 45-minute in-depth coverage of some method of analysis. Beginning tutorials on R are occasionally offered as well. Membership is free of charge, but donations are accepted to defray the cost of snacks and web site maintenance. You can join at the KRUG web site.

Data Science KNX is a group of people interested in the broad field of data science. Members range from beginners to experts. As their web site states, their “…aim is to maintain a forum for connecting people around data science specific topics such as tutorials and their applications, local success stories, discussions of new technologies, and best practices. All are welcome to attend, network, and present!” You can go here to join at the Data Science KNX web site. Membership is free, though the group gladly accepts donations to help defray the costs of the pizza and beer provided at their meetings.

The East Tennessee Business Intelligence Users Group is “committed to learning, sharing, and advancing the field of Business Intelligence in the East Tennessee region.” They meet several times each year featuring speakers who demonstrate business intelligence software such as IBM’s Watson and Microsoft’s PowerBI. Meetings are at lunch and a meal is provided by sponsoring companies. Membership is free, and so is the lunch! You can join the group at their web site.

Each spring and fall, The University of Tennessee’s Department of Business Analytics and Statistics offers a Business Analytics Forum that features speakers from both industry and academia. The group consists of non-competing companies for whom business analytics is an important part of their operation. Forum members work together to share best practices and to develop more effective strategies. The forum is open to paid members only and you can join on their registration page.