Teaching Statement

On the horizon are enormous challenges and opportunities for those of us involved in data science. They are wound too tightly together to be clearly distinguished. On the one hand, there is interest on the part of students, business people, and even the general public as never before. One need not be very old to remember the days when statistics was seen as a dry discipline of interest to few. On the other hand, the replication crisis continues to unfold and presents as ever more pervasive. This has the potential to produce a crisis of faith in data science and in science more generally.

Any teacher of an introductory course is implicitly in the business of selling the discipline. Our salesmanship must be honest. It is thus paramount to communicate to students what these techniques can do as well as what they cannot and further to communicate that even this boundary is not well-understood. Students must also understand that data analytics is first a way to understand data and only then a means of testing hypotheses.

There is another important element to this. While many disciplines and business concerns use data science to support decisions, the relationship between the methods themselves and the decisions lies outside the boundaries of the science considered strictly. This is where domain area knowledge or an “area of application” in more traditional statistical parlance comes in. Having worked with data both in industry and in academia, I have spent a great deal of time thinking about this relationship and I endeavor to communicate this difficulty to students.

Ultimately, students must understand that data analytic work starts and supports but does not end conversations. Even results that may seem decisive are only so with respect to some pre-existing model of the world. It is important to understand then that it is those two things together, not the data analysis alone that are decisive. The mystique of data science lends itself to magical thinking. As teachers, we must combat this thinking in the name of intellectual and professional honesty.

For this reason, I understand data science as a Socratic discipline and an applied branch of the philosophy of science. In some sense, the very framing of the replication crisis is misleading. While there has clearly been a great deal of truly problematic work, even the most carefully done work will sometimes fail to replicate. Also, failure to replicate once is hardly decisive. We rather need to think of models and methods as part of an ongoing conversation that encapsulate some of our current thinking. It is never appropriate to promulgate an “official scientific position.” Therefore, our goal as teachers of data science is to inculcate in students an attitude toward data analytics that is simultaneously careful, conservative, and also ecumenical; supporting of decisions only as intermediated by conversations. This is both an immense challenge and an immense privilege.

Click on the course name for more information.

Econ 100 (Economics for the Citizen)
This is a principles level economics course aimed at non-economics majors. This allows GMU students to fulfill their social science requirement. It covers all of the standard material in a principles level microeconomics course with a focus on how this information is valuable to all citizens and not merely to students intending advanced study in economics. 
Econ 385 (International Economic Policy)
This course is intended for students majoring in public policy and related disciplines. It covers the history and practice of international trade; including trade deals and how international trade can be wealth enhancing. The majority of the course tends toward a favorable view of trade. The final reading is an ethnographic reading describing how exposure to trade can possibly undermine a culture that itself was highly trade dependent. Thus, the course ends on an ambiguous note to help students understand that nothing in the human sciences is cut and dry.
Econ 212 (Intermediate Microeconomics)
This is the core course of the economics major in that all of the more advanced courses build upon it. It covers partial equilibrium, consumer and producer theory and a little bit of general equilibrium theory. Students also study externalities and Coasean bargaining. An unusual feature of my course is that I also teach the Bloomington School analysis of common pool resource issues.
Econ 314 (Money and Banking)
This is an advanced elective intended primarily for economics majors. My course is non-standard. It begins with a discussion of gold-backed free banking. I believe that the concreteness of this arrangement where bank notes are fractional claims on gold sitting in a vault helps students understand how banking works both historically and today. Also covered is some of the history of bank runs, and the question of whether it is desirable that the state play a role in the monetary system. The course ends with a discussion of various macroeconomic analyses of the role of money in business cycles. The goal of this final section is to stress to students that macroeconomics is an area of active research and not settled science.
MAT-300 (Regression Analysis)
This is the second course in introductory statistics. The course covered regression analysis, inference, model comparison and model diagnostics. It provides a good foundation for more advanced courses in statistics and machine learning.
DAD-215 (Introduction to SAS)
This is an introduction to the SAS system;  software for statistics and data management commonly used in industry. The goal of the course is to take students with some knowledge of statistics and give them the tools to apply that knowledge to real world problems. It can greatly enhance their employment prospects. Topics covered are the data step and various analytic procedures. At the end of the course, the students have to produce a substantial piece of analysis.
Contact Me

(571) 207-5640

Department of Economics
George Mason University
4400 University Dr
Fairfax, VA 22030

I am interested in academic and industrial work; including consulting roles. Feel free to contact me with opportunities.