Lindsay Hiser
Lindsay Hiser, MPP Class of 2023


Data and Programming for Public Policy I - R (PPHA 30535) is the first installment in a data science course sequence at Harris where students learn to program in the coding language R.  Lindsay Hiser, MPP Class of 2023, shares her experience taking the course with Associate Professor Peter Ganong during the Spring 2022 quarter. If you’re interested in experiencing a class with Peter Ganong, attend his Masterclass on Thursday, August 11 to learn about the impact of public policies on people facing financial difficult financial circumstances. 

I enrolled in the R sequence of Data and Programming in the spring quarter of my first year at Harris. One sequence of the course, either in R or Python, is a requirement for students pursuing the Certificate in Data Analytics at Harris.

Having just completed the Core, which included introductory statistical exercises in R, I wanted to deepen my understanding of the programming language and how it can be used in data analysis. Whereas the Core utilized R for specific exercises, this Data and Programming course was an opportunity to learn R from the ground up—that is, to understand its fundamental structure, syntax, and tools in order to creatively and effectively do data science for different purposes. As someone who is interested in data science, the comprehensive approach offered by the course was part of its appeal.

Peter Ganong
Associate Professor Peter Ganong

Professor Ganong was intentional and thoughtful in creating a classroom environment where all questions—even the most minor of clarifications—were encouraged. The lectures focused on how to clean, analyze, and visualize large data sets in R, closely following Wickham and Grolemund’s R for Data Science text, available free online. Although I appreciated the open source availability of the textbook, I found that the interactive, in-person lectures were a crucial part of my own learning. Professor Ganong's lectures were helpful for clarifying confusing concepts and solidifying my own understanding. 

Professor Ganong also introduced real-world applicability to the classroom through a series of guest lectures. Over the course of the quarter, we welcomed professionals working in data science to speak about their work and careers. Speakers came from a variety of organizations, including the City of Chicago’s Office of the Inspector General, The Upshot from The New York Times, ProPublica, and the Chicago Transit Authority. 

Similar to class lectures, student questions during guest lectures were highly encouraged and often shaped subsequent class discussions. Assigned problem sets applied the datasets and lessons our guest  lecturers used in their own work. The guest lecture series exemplified one goal of the course: to teach policy students how to use data to improve the performance of public service organizations. 

This fall, I’ll be enrolling in the second course of the Data and Programming in R sequence. In the meantime, I continue to use R on a regular basis as part of my research fellowship with the Energy Policy Institute at the University of Chicago