POLISCI 344: Politics and Geography

Graduate class on the role of geography in topics in political economy, including development, political representation, voting, redistribution, regional autonomy movements, fiscal competition, and federalism.

Role: Co-Instructor; wrote new curriculum for GIS in R (previously taught in ArcGIS)

Teacher Evaluation Scores

  • Average Learning Quality Score: 5.0 / 5.0
  • Average Instructional Effectiveness Score: 4.8 / 5.0

Teacher Evaluation Comments

Course Materials

POLISCI 241S / ANTHRO 130D / URBANST 134: Spatial Approaches to Social Science

This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.

Role: Lab Assistant; Extensive out-of-class support for student projects (primary learning goals are met through student projects).

Teacher Evaluation Scores

  • Average Learning Quality Score: 4.3 / 5.0
  • Average Instructional Effectiveness Score: 4.5 / 5.0

Teacher Evaluation Comments

Computational Methods Boot Camp

Boot camp to provide all incoming Vanderbilt Political Science graduate students with a solid foundation in computational methods before the start of the academic year. Focused on developing familiarity with basic functional of R, including basic data types, data manipulation, and plotting.

Role: Instructor

Teacher Evaluations

Practical Data Science 1

Duke Masters in Interdisciplinary Data Science (MIDS) fall semester course. Data Science is an intrinsically applied field, and yet all too often students are taught the advanced math and statistics behind data science tools, but are left to fend for themselves when it comes to learning the tools we use to do data science on a day-to-day basis or how to manage actual projects. This course is designed to fill that gap by providing students with extensive hands-on experience manipulating real (often messy, error ridden, and poorly documented) data using the a range of bread-and-butter data science tools (like the command line, git, python (especially numpy and pandas), jupyter notebooks, and more).

Role: Instructor, Creator

Mid-semester Survey Results

Select Student Comments from Evaluations

  • “Nick is the best TA I’ve had at Stanford. He’s extremely attentive in class while students work on labs. He tended to students’ questions and addressed students’ concerns consistently, effectively, and efficiently. Nick was much more in tune with students’ projects than the professor and helped students overcome technical challenges and conduct difficult quantitative analyses. The fact that everyone had their own projects, and therefore unique questions, speaks particularly highly to Nick’s ability to serve students with a broad scope of
    challenging questions. Every student I spoke to in the class felt similarly.”
  • “He was good at helping me find direction with my group project and helping bridge the gap between interesting thematic subject matter and the technical/methodological ways of designing and executing a project that
    explored these goals. He was really good at using simple, basic language to explain big concepts or ideas or to explain technological processes in ARCGIS.”
  • “Nick was the best TA I have had at Stanford! He was always well-prepared; he stayed in constant communication with the class so that we knew exactly what was expected; he was available whenever we needed him; and his knowledge of the material and his ability to share that with us were excellent. He was very friendly and easy to talk to”
  • “I came to the class entirely unfamiliar with ArcGis and fairly insecure about my ability to leverage software systems to conduct quantitative analysis. Nick explained answers to my questions clearly without being condescending. He made big challenges feel manageable. His comfort with the subject material clearly transferred in his ability to answer questions effectively and confidently.”