Andreas Ferrara

Dr. Andreas "Andy" Ferrara is an economic historian working on topics related to labor economics and political economy including migration, discrimination, and culture. He received his Ph.D. in economics from the University of Warwick, UK, and has been Assistant Professor in the Department of Economics at the University of Pittsburgh since the fall of 2019. His work has been published in the Quarterly Journal of Economics, the Review of Economics and Statistics, and the Journal of Labor Economics, among others. He has recently been appointed as a Faculty Research Fellow in the NBER’s program on the Development of the American Economy.

1. What course[s] do you teach in the MQE program?

 I have been teaching the Big Data and Forecasting in Economics class since the start of the program. The main aim is to introduce students to a wide range of machine learning methods commonly used for prediction in the private and public sector, and to develop a working knowledge of the programing language Python. Most of the other classes are using R, hence learning a second language makes our graduates more competitive in the labor market.

2. What do you love most about teaching in the MQE program?

 Seeing the remarkable growth of our students during the term is always extremely gratifying. In the first week, students oftentimes struggle with Python as they get used to a new programing language. But then by the end of the term, they tend to be very proficient in the language, cleaning data and running complex machine learning models, such as neural networks or support vector machines. It always makes me feel like they learned something in my class that’s going to benefit them in their future careers.

3.  What is the biggest takeaway you hope students will gain from in your class?

 I hope that they will internalize the general workflow of a machine-learner or data scientist in general. Once they know these kinds of steps as well as the power of cross-validation (I won’t get into the details, it would be too nerdy), then there is very little they cannot tackle in the real world with the methods that we see in class together.