All posts tagged teaching

Stop Teaching R. Teach Python.

There has been a slight transition in social science teaching since I have been a student and professor over the past ~15+ years. In the aughts, it was still common to teach students in legacy, closed source statistical software (SPSS, SAS, and Stata). When I was a PhD student in criminal justice at SUNY Albany, we had a specific class to learn SPSS, although most of the rest of the quantitative courses used Stata.

The R programming language has likely usurped the use of the closed source languages in social science education after the aughts though. (I do not have hard data, but that is my impression seeing what colleagues are using and what they teach in classes.)

I am familiar with all of the major statistical programs (I have written an R package, and you can see this blog for many examples of SPSS and a few for Stata). If the goal in coursework is to teach your students skills relevant to help them get a job, academics in social science institutions should teach their students Python. The current job market for quantitative work is dominated by Python positions.

To be clear, I am not fundamentally opposed to closed source programming languages (there are scenarios where SPSS/SAS make more sense than Hadoop systems I have seen, also if you are a GIS analyst you should learn ESRI tools). This is purely just an observation given the current private sector job market – focusing primarily on Python makes the most sense for social science students.

As an experiment, I went onto LinkedIn and did a search for “data scientist”. Your results will differ (mine are tailored to the Raleigh area, and also includes more senior positions), but here is a table of the positions that came up on the first page, and a quick summary of the tech stacks they require. While this is not a systematic sample, it gives a reasonable snapshot of current expectations.

| Company             | Job Title                           | Tech Stack                                           | URL                                            |
|---------------------|-------------------------------------|------------------------------------------------------|----------------------------------------------- |
| Google              | Data Scientist (Google Voice 2)     | Python, R, SQL                                       | https://www.linkedin.com/jobs/view/4387751995/ |
| Deloitte            | AI Specialist                       | None specified                                       | https://www.linkedin.com/jobs/view/4376183670/ |
| Ascensus            | Principal Analytics                 | R, Python, SQL, GenAI/LLM                            | https://www.linkedin.com/jobs/view/4380164400/ |
| EY                  | AI Lead Engineer                    | Python, C#, R, GenAI/LLM                             | https://www.linkedin.com/jobs/view/4385954762/ |
| PwC                 | GenAI Python Systems Engineer (2)   | Python, SQL, Cloud Platforms, GenAI/LLM              | https://www.linkedin.com/jobs/view/4373604638/ |
| Affirm              | Senior Machine Learning Engineer    | Python, Spark/Ray                                    | https://www.linkedin.com/jobs/view/4326673670/ |
| Lexis Nexis         | Lead Data Scientist                 | Cloud Platforms, GenAI/LLM                           | https://www.linkedin.com/jobs/view/4316327742/ |
| EY                  | AI Finance                          | SQL, Python, Azure, GenAI/LLM                        | https://www.linkedin.com/jobs/view/4385085950/ |
| Korn Ferry          | Sr. Data Scientist                  | Python, R, Spark, AWS, GenAI/LLM                     | https://www.linkedin.com/jobs/view/4387433496/ |
| Deloitte            | Data Science Manager                | Python, Cloud                                        | https://www.linkedin.com/jobs/view/4304674642/ |
| First Citizens Bank | Senior Quant Model Developer        | Python, SAS, SQL                                     | https://www.linkedin.com/jobs/view/4365378242/ |
| First Citizens Bank | Senior Manager Quant Analysis       | Python, SAS, Tableau                                 | https://www.linkedin.com/jobs/view/4388131284/ |
| Jobot               | ML Solution Architect               | Python, Scala, Spark, AWS, Snowflake                 | https://www.linkedin.com/jobs/view/4384023540/ |
| Affirm              | Analyst II                          | SQL, Python, R, CPLEX/Gurobi, Databricks/Snowflake   | https://www.linkedin.com/jobs/view/4373303038/ |
| Red Hat             | Sr Machine Learning Engineer (vLLM) | Python, GenAI/LLM                                    | https://www.linkedin.com/jobs/view/4354827922/ |
| Alliance Health     | Director AI                         | Python (TensorFlow/PyTorch), Office Products, GenAI  | https://www.linkedin.com/jobs/view/4383011480/ |
| Nubank              | ML Data Engineer                    | Python, Ray/Spark                                    | https://www.linkedin.com/jobs/view/4376815752/ |
| Target RWE          | Senior Quant Data Scientist         | R                                                    | https://www.linkedin.com/jobs/view/4385293724/ |
| Siemens             | Senior Data Analytics               | SQL, Python, R, Tableau/PowerBI                      | https://www.linkedin.com/jobs/view/4377969531/ |
| Red Hat             | Sr Machine Learning Engineer        | Python, GenAI/LLM                                    | https://www.linkedin.com/jobs/view/4302769773/ |
| Lexis Nexis         | Director Data Sciences              | Python, R, GenAI/LLM                                 | https://www.linkedin.com/jobs/view/4387335028/ |
| Cigna               | Data Science Senior Advisor         | Python, SQL                                          | https://www.linkedin.com/jobs/view/4381766145/ |
| Thermo Fisher       | Senior Manager Data Engineering     | Fabric, PowerBI, Python, Databricks, Tableau, SAS    | https://www.linkedin.com/jobs/view/4372684009/ |

Of the positions:

  • 9/25 roles included R, but only one required R exclusively. The other 8 were Python/SQL/R
  • 22/25 included Python
  • 11/25 had a focus on Generative AI or LLMs

Python dominates R in the current job market for data science positions. Professors are doing their students a disservice teaching R, the same way they would be doing a disservice teaching their students to code in Fortran.

Another aspect I noticed for this – analyst type jobs not all that long ago really only expected Excel (and maybe SQL). Now even the majority of the analyst jobs expect Python (even more so than dashboard tools like PowerBI in this sample).

For individuals on the job market, I suggest going and doing your own experiment job search like this on LinkedIn to see the tech skills you need to be able to at least get your foot in the door for an interview. I expected GenAI to be slightly more popular (only 11/25), but there were a few other technologies sprinkled in enough it may be good to become familiar with to widen your potential pool (Cloud and Spark – I am surprised Databricks was not listed more often).

If you’re looking to build Python skills from scratch, I cover this in my book: Data Science for Crime Analysis with Python (can purchase in paperback or epub at my store).

If also interested in learning about generative AI, see my book Large Language Models for Mortals: A Practical Guide for Analysts with Python.

You can use the coupon TWOFOR1 to get $30 off when purchasing multiple books from my store.

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by Andy Wheeler on March 22, 2026  •  Permalink
Posted in data science, Personal Productivity, Python, R
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Posted by Andy Wheeler on March 22, 2026

https://andrewpwheeler.com/2026/03/22/stop-teaching-r-teach-python/

My online course lab materials and musings about online teaching

I often refer folks to the courses I have placed online. Just for an update for everyone, if you look at the top of my website, I have pages for each of my courses at the header of my page. Several of these are just descriptions and syllabi, but the few lab based courses I have done over the years I have put my materials entirely online. So those are:

And each of those pages links to a GitHub page where all the lab goodies are stored.

The seminar in research focuses on popular quasi-experimental designs in CJ, and has code in R/Stata/SPSS for the weekly lessons. (Will need to update with python, I may need to write my own python margins library though!)

Grad GIS is mostly old ArcGIS tutorials (I don’t think I will update ArcPro, will see when Eric Piza’s new book comes out and just suggest that probably). Even though the screenshots are perhaps old at this point though the ideas/workflow are not. (It also has some tutorials on other open source tools, such as CrimeStat, Jerry’s Near Repeat Calculator, GeoDa, spatial regression analysis in R, and Mallesons/Andresens SPPT tool are examples I remember offhand.)

Undergrad Crime Analysis is mostly focused on number crunching relevant to crime analysts in Excel, although has a few things in Access (making SQL queries), and making a BOLO in publisher.

So for folks self-learning of course use those resources however you want. My suggestion is to skim through the syllabus, see if you want to learn about any particular lesson, and then jump right to that one. No need to slog through the whole course if you are just interested in one specific thing.

They are also freely available to any instructors who want to adapt those materials for their own courses as well.

One of the things that has disappointed me about the teaching response to Covid is instead of institutions taking the opportunity to really invest in online teaching, people are just running around with their heads cut off and offering poor last minute hybrid courses. (This is both for the kiddos as well as higher education.)

If you have ever taken a Coursera course, they are a real production! And the ones I have tried have all been really well done; nice videos, interactive quizzes with immediate feedback, etc. A professor on their own though cannot accomplish that, we would need investment from the University in filming and in scripting the webpage. But once it is finished, it can be delivered to the masses.

So instead of running courses with a tiny number of students, I think it makes more sense for Universities to actually pony up resources to help professors make professional looking online courses. Not the nonsense with a bad recorded lecture and a discussion board. It is IMO better to give someone a semester sabbatical to develop a really nice online course than make people develop them at the last minute. Once the course is set up, you really only need to administer the course, which takes much less work.

Another interested party may be professional organizations. For example, the American Society of Criminology could make an ad-hoc committee to develop a model curriculum for an intro criminology course. You can see in my course pages I taught this at one point – there is no real reason why every criminology teacher needs to strike out on their own. This is both more work for the individual teacher, as well as introduces quite a bit of variation in the content that crim/cj students receive.

Even if ASC started smaller, say promoting individual lessons, that would be lovely. Part of the difficulty in teaching a broad course like Intro to Criminology is that I am not an expert on all of criminology. So for example if someone made a lesson plan/video for bio-social criminology, I would be more apt to use that. Think instead of a single textbook, leveraging multi-media.

It is a bit ironic, but one of the reasons I was hired at HMS was to internally deliver data science training. So even though I am in the private sector I am still teaching!

Like I said previously, you are on your own for developing teaching content at the University. There is very little oversight. I imagine many professors will cringe at my description, but one of the things I like at HMS is the collaboration in developing materials. So I initially sat down with my supervisor and project manager to develop the overall curricula. Then for individual lessons I submit my slides/lab portion to my supervisor to get feedback, and also do a dry run in front of one of my peers on our data science team to get feedback. Then in the end I do a recorded lecture – we limit to something like 30 people on WebEx so it is not lagging, but ultimately everyone in the org can access the video recording at a later date.

So again I think this is a better approach. It takes more time, and I only do one lecture at a time (so take a month or two to develop one lecture). But I think that in the end this will be a better long term investment than the typical way Uni’s deliver courses.

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by Andy Wheeler on January 14, 2021  •  Permalink
Posted in Crime Analysis, Crime Mapping, Criminal Justice, scholarly
Tagged , ,

Posted by Andy Wheeler on January 14, 2021

https://andrewpwheeler.com/2021/01/14/my-online-course-lab-materials-and-musings-about-online-teaching/