On Teaching Data Science

Build Good Habits from the Beginning

In the first week of the course, I spent no time on programming, introduced students to the world of data science, and left a lot of time for Q&A. I taught them about proprietary vs. open-source software, data analysis tools, the importance of mathematics in data science, and the data pipeline.

We spent the second week on software configuration and installation. We went through all of the details of installing the software, setting up an ODBC connection to students’ laptops, setting up version control with R, submitting simple code to GitLab. We then proceeded to learn about R, making connections with prior programming knowledge.

Industry Tools

In addition, I wanted students to come out of my class to understand the tools used in the industry. While I could spend the entire semester teaching R, only covering R would be a disservice to my students.

I believe that a difficult concept can seem easier when one tackles a slightly more challenging problem with a little more time. Often, a more complex problem can provide perspective, making what used to appear complicated seem easy. Thus, with the interest of teaching students as many tools as possible in one semester, I decided that I would teach

• Git + GitHub (in Fall 2019) or GitLab (in Spring 2020 and afterward)
• Data manipulation (R: Base R, dplyr, data.table; T-SQL; Excel; Access; Python: NumPy, pandas)
• Data visualization (R: ggplot2, Python: matplotlib)
• Data reporting (R Markdown, Jupyter Notebook)
• String manipulation, including RegEx (R, Python)
• Date parsing (R, Python)

all in one semester. This content comprises what would usually be taught over five to six semesters of coursework, all in a single semester. However, the approach to teaching is quite simple: using the assumed programming background, explain how R is similar to C, and show everyday data manipulation tasks in R. Then, perform those same data manipulation tasks in T-SQL, Excel, Access, and Python. Additionally, one reinforces version control principles (except for Excel and Access) and pushes content to GitHub or GitLab. By introducing these topics this way, students don’t view the data manipulation tools as independent tools but rather as tools that perform similar tasks. While R remains the primary emphasis of the course, students understand how other tools perform similar tasks as well.

I emphasized the use of best industry practices throughout the semester, such as version control: students submitted all assignments through GitHub (in Fall 2019) or GitLab (in Spring 2020 and afterward). In addition, I wanted students to have the experience of writing actual queries, so I learned how to set up a database in Microsoft Azure for the class.

No Textbooks

It then became apparent that no textbook or collection of textbooks would suffice to teach the material that I wanted to cover for DSCI 2000. Thus, I spent most of Summer 2019 translating all of the concepts in my head to LaTeX Beamer slides. These notes would serve as the “textbook” for the course at no cost to my students. They would also serve as the official source for any modes of assessment that I provide my students, such as homework or quizzes. I am thankful to every professor in my master’s program, as all of them approached their courses this way, and while I was a student, having such notes helped me prioritize and find clarity in very dense material.

If my students wanted textbooks, I had a page-long list of books available (mostly free, open-source texts) with the syllabus.

No Exams

Considering that I covered at least four semesters of material in this class, I opted not to give my students any high-pressure, timed exams for DSCI 2000 to focus on learning the tools that I taught them in class. This idea is one I credit to Dr. Karin Dorman, who was my professor for STAT 580 (Statistical Computing) in graduate school.

To ensure students are keeping up with the material, I provided students four quizzes (30 minutes each) throughout the semester. I provided a study guide of about 20-30 questions; 10 of these questions constituted the actual quiz, and students needed only to answer 7 of them correctly to obtain a 100% on a quiz. I also provided them extra credit questions that do not come from the study guides.

In Fall 2019, quizzes were in person. In Spring 2020, I proctored the first few quizzes but then transitioned to half-hour oral quizzes on Zoom when COVID-19 hit. Over Fall 2020 and Spring 2021, I resorted to half-hour oral quizzes on Zoom throughout the entire semester. I found they were very efficient and required students to focus on their communication skills more than standard paper-and-pencil quizzes.

Final Project

In Fall 2019, I had students choose a topic of interest, search for their own publicly available data sets, and give an oral and written report (using R Markdown) on their findings. Based on student feedback and to avoid burdening students with having to look for data, I aimed for a different approach in Spring 2020.

In Spring 2020 and afterward, I gave students a simulated job interview via Zoom, consisting of a few behavioral questions (grading based only on participation) and interview questions based on the material they learned in class. I set the expectation for all of the students that I did not expect any of them to answer all of the questions correctly and that I would curve the grades appropriately, and as far as I could tell, this approach was well-received.

Real-World Homework Assignments

Homework assignments that I provided the students were representative of things that I have had to do as a professional. They included:

• Learning about a new data-science topic on your own: I provided a list of current-day problems in data science and asked students to write an essay about a problem of their choice, and delivered them my feedback from my experience as a professional. There was no expectation that students would get everything correct about their topic, but it was necessary to teach students how to learn about topics independently. Essay topics included explainable AI, the GDPR, R vs. Python in data science, and the curse of dimensionality.

• Analyzing jobs in data science: I had my students go to a website such as LinkedIn and Indeed and asked them to find some job postings that interested them. Then, I had my students analyze the job descriptions based on the material I taught them in class.

• A data analysis report: my students submitted an R Markdown report where they documented their steps of a data analysis (reading in a file or database table, tidying up the data, visualizing it, and performing a statistical analysis) through GitHub or GitLab. Topics included actuarial science life tables (Fall 2019), working with daily COVID-19 data from Johns Hopkins University (Spring 2020), correlation of stock data with unemployment rates (Fall 2020), and correlations between multiple stocks (Spring 2021).

Participation in Contests

In Fall 2019, Spring 2020, and Spring 2021, I had teams of students in my courses participate in contests. In particular, during the spring contests, my students placed in contests against teams of students from four-year institutions, some of whom were graduate students. Because of how demanding these contests were, I waived homework and final project requirements for students who participated in them.