**Official Description**: Seminar—3 hours; discussion/laboratory—1 hour. Prerequisite: graduate standing. Introductory seminar introducing data analysis methods critical to basic empirical investigations in political science.—I.

**Instructors**: Kyle Joyce

**Other info and comments**: Offered every Fall. From the syllabus, found here:

*This is the first of three required courses in the Department of Political Science’s political methodology sequence. This course is an introduction to probability theory and mathematical statistics. We will cover the basic concepts in statistics including probability, discrete and continuous probability distributions, random variables, moments, hypothesis testing, and inference. These topics form the foundation on which all statistical work is based. This foundation will be important for understanding the material in the other classes in the methods sequence, for understanding any advanced methods courses you take in the future, for making you more informed readers of political science research, and, importantly, for improving the statistical analysis you do in your own research. Note: I assume no prior mathematical skills beyond those covered in the Math Camp.*

**Software used: **R

Really good intro to probability theory course. Should be useful for natural scientists as well as social scientists. Kyle uses a lot of student in-class work (as in, go to the board and solve this problem in front of the class), which is uncomfortable at times, but ensures that the instruction and students proceed in parallel. As the first-quarter requirement for PoliSci students, this also serves as a nice gentle introduction to R and LaTex.