• Instructor: Benjamin Soltoff, Lecturer in Computational Social Science
• Meeting day/time: TTh 2-3:20pm (Social Sciences Research Building 107)
• Office hours: M 10-12pm, Tu 9-10am (McGiffert House 209)
• Prerequisites: MACS 33000 (Computational Mathematics and Statistics Camp)
• Requirements: Bring your own laptop

## Course Description

This course aims to provide students with a core understanding of mathematics and statistics for computational social science. Students who complete this course should be prepared to take more advanced computational methods courses.

## Course Objectives

By the end of the course, students will:

• Construct and execute basic programs in R using elementary programming techniques and tidyverse packages (e.g. loops, conditional statements, user-defined functions)
• Visualize information and data using appropriate graphical techniques
• Generate reproducible documents with R Markdown
• Utilize professional document typesetting with mathematical notation via Markdown and $$\LaTeX$$
• Define key concepts related to theory of probability
• Identify and simulate random variables
• Calculate expected value and variance of random variables
• Write likelihood functions and optimize using a range of algorithms
• Conduct hypothesis tests
• Implement Bayesian methods of inference
• Evaluate via simulations the major properties and assumptions of ordinary least squares (OLS) regression
• Conduct inference and hypothesis testing for single and multivariable regression models