Teaching is not simply about our instruction techniques, but the way we enable learning in the university community. To that end, I have created new courses, a new major, and worked hard to improve the diversity of our student body.
I have taught four courses at NYU, two of which I created. These include Introduction to Research Methods (POL850, undergrad) and Text as Data (DS1015, graduate). I was also part of the leadership team that created the new major in Data Science—now pending state approval. This major will be a popular offering, and because majors are required to take a CAS minor, it will boost demand for both STEM and non-STEM. Within the work for that major (and its minor), I helped design Data Science for Everyone and Causal Inference.
Data Science for Everyone builds on the Berkeley smash-hit Data8. It introduces students to fundamentals of statistics and inference, but with a modern computational element, such that they simultaneously learn ‘real’ programming. We also cover important practical issues like ethics. My promise is that Data Science for Everyone will be the most popular course in the entire university by enrollment numbers within five years. We will teach 150 students in the fall, but will scale to 400 a semester soon after. Beyond the academic content of the course, every student, regardless of their background will come to understand that they belong in the DS revolution and that they have something to contribute. Data Science represents no less than an opportunity to build—virtually from scratch—a discipline that will affect all inquiry. My task has been and still is to ensure NYU leads this effort.
What do I Want Students to Learn?
I want students to come away with two lessons: (a) be skeptical about everything (b) with the exception of their place in data science.
One of the gifts of an education is the ability to sensibly question what elites tell us is true. My goal is not to make my students nihilistic: that is, they do not leave my courses believing nothing is knowable, and everything is a matter of opinion. Rather, it is to give them the tools to be informed citizens. Part of this is the idea that knowledge—of statistical techniques specifically—is powerful. It enables historically underrepresented groups (including women and minority students) to question powerful political, policy and economic interests.
Why is Teaching Important?
Teaching matters because it shapes discipline via the students we recruit and train. Here, I have been conscious in my efforts on outreach and admissions to diversify. Working hard with the previous Director (Rich Bonneau) to reach out to applicants from under-represented groups, and soliciting and then offering scholarship funds from donors in some cases, I was able to increase the presence of such students to somewhere between 5 and 10 percent of the program population. This is not enough, but I am proud to have been involved in these efforts and I will continue to do so.