- 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

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