Publications by Bob Muenchen
Forrester’s 2017 Take on Tools for Data Science
In my ongoing quest to track The Popularity of Data Science Software, I’ve updated the discussion of the annual report from Forrester, which I repeat here to save you from having to read through the entire document. If your organization is looking for training in the R language, you might consider my books, R for SAS and SPSS Users or R for Sta...
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The Tidyverse Curse
I’ve just finished a major overhaul to my widely read article, Why R is Hard to Learn. It describes the main complaints I’ve heard from the participants to my workshops, and how those complaints can often be mitigated. Here’s the only new section: The Tidyverse Curse There’s a common theme in many of the sections above: a task that is ha...
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Python and R Vie for Top Spot in Kaggle Competitions
I’ve just updated the Competition Use section of The Popularity of Data Science Software. Here’s just that section for your convenience. Competition Use Kaggle.com is a web site that sponsors data science contests. People post problems there along the amount of money they are willing pay the person or team who solves their problem the best. B...
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Keeping Up with Your Data Science Options
The field of data science is changing so rapidly that it’s quite hard to keep up with it all. When I first started tracking The Popularity of Data Science Software in 2010, I followed only ten packages, all of them classic statistics software. The term data science hadn’t caught on yet, data mining was still a new thing. One of my recent bl...
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Group-By Modeling in R Made Easy
There are several aspects of the R language that make it hard to learn, and repeating a model for groups in a data set used to be one of them. Here I briefly describe R’s built-in approach, show a much easier one, then refer you to a new approach described in the superb book, “>R for Data Science, by Hadley Wickham and Garrett Grolemund. F...
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Dueling Data Science Surveys: KDnuggets & Rexer Go Live
What tools do we use most for data science, machine learning, or analytics? Python, R, SAS, KNIME, RapidMiner,…? How do we use them? We are about to find out as the two most popular surveys on data science tools have both just gone live. Please chip in and help us all get a better understanding of the tools of our trade. For 18 consecutive year...
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Data Science Tool Market Share Leading Indicator: Scholarly Articles
Below is the latest update to The Popularity of Data Science Software. It contains an analysis of the tools used in the most recent complete year of scholarly articles. The section is also integrated into the main paper itself. New software covered includes: Amazon Machine Learning, Apache Mahout, Apache MXNet, Caffe, Dataiku, DataRobot, Domino D...
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jamovi for R: Easy but Controversial
jamovi is software that aims to simplify two aspects of using R. It offers a point-and-click graphical user interface (GUI). It also provides functions that combines the capabilities of many others, bringing a more SPSS- or SAS-like method of programming to R. The ideal researcher would be an expert at their chosen field of study, data analysis, ...
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Gartner’s 2018 Take on Data Science Tools
I’ve just updated The Popularity of Data Science Software to reflect my take on Gartner’s 2018 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging though all 40+ pages of my report, here’s just the new section: IT Research Firms IT research firms study software products and corporate st...
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Using Excel for Data Entry
This article shows you how to enter data so that you can easily open in statistics packages such as R, SAS, SPSS, or jamovi (code or GUI steps below). Excel has some statistical analysis capabilities, but they often provide incorrect answers. For a comprehensive list of these limitations, see http://www.forecastingprinciples.com/paperpdf/McCull...
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