- 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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