PS 300

Fall 2025 All Classes

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Credit: 3 hours.

Selected readings and research in political science. See Class Schedule for current topics.

May be repeated to a maximum of 6 hours if topics vary. Prerequisite: Six hours of political science, or consent of instructor.

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PS 300 class schedule data for fall 2025
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	56260	Lecture-Discussion	FV	5:00PM -6:20PM	MW	70B Wohlers Hall	Vasselai, F	Part of Term: 1 Date Range: 08/25/25-12/10/25 Section Title: Data Sci.Methods & Mach.Learn. Section Info: Title: Data Science Methods & Machine Learning. Course will be taught by Professor Fabricio Vasselai. This course is designed as a gentle, applied introduction to key computational concepts and methods in Data Science and Machine Learning, geared towards Social Scientists. The rapid increase in the variety and sophistication of tools at our disposal is transforming the Social Sciences, making them increasingly computational. The goal of the course is to provide students, even those with little quantitative background, with the knowledge and skills necessary to 1. understand and apply the basic versions of each of a variety of Data Science techniques; 2. be able to later pursue further training in case they decide to learn some of those methods more in-depth. The course assumes that students have already been introduced to the R programming language, but we will spend two weeks doing a fast-paced but thorough review of base R and programming logic (free online R tutorials will also be made available). Next, students will learn critical Data Science techniques, such as advanced data handling and cleaning, data visualization and web-scraping, as well as how computer hardware works. In the second part of the course, I introduce Monte Carlo simulations and bootstrapping, as well as the basics of text analysis, network analysis, and spatial data. The third and final part of the course focuses on introducing students to Machine Learning, covering key topics such as clustering, dimensionality reduction, k-nearest neighbors, decision trees, random forests, nonlinear regression, and basic deep neural networks. Prerequisites: PS 230 or introductory social research methods course; STAT 200, ECON 203 or other course introducing linear regression (OLS); and prior knowledge of the R programming language. Restriction(s): Not intended for students with Freshman class standing.
	56257	Lecture-Discussion	PD	2:00PM -3:20PM	MW	223 David Kinley Hall	Diehl, P	Part of Term: 1 Date Range: 08/25/25-12/10/25 Section Title: Approaches to Peace Section Info: This course offers a survey of the various approaches to peace on the international level. It begins by looking at the different kinds of peace from a variety of perspectives. Then two sections of the course are organized according to the major distinction between “negative peace” (the absence of war or violence – conflict management) and “positive peace” (justice, dispute resolution, reconciliation – conflict resolution). Mechanisms (e.g., mediation) to achieve both kinds of peace are explored. Restriction(s): Not intended for students with Freshman class standing.