NOTE: Course information changes frequently, including Methods of Instruction. Please revisit these pages periodically for the most recent and up-to-date course information. | |
Fall 2023 Quantitative Methods: Social Sciences GR5058 section 001 DATA MINING FOR SOCIAL SCIENCE DATA MINING FOR SOCIAL SC | |
| Call Number | 13809 |
| Day & Time Location |
T 8:10pm-10:00pm |
| Points | 4 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Benjamin K Goodrich |
| Type | SEMINAR |
| Method of Instruction | In-Person |
| Course Description | The class is roughly divided into three parts: 1) programming best practices and exploratory data analysis (EDA); 2) supervised learning including regression and classification methods and 3) unsupervised learning and clustering methods. In the first part of the course we will focus writing R programs in the context of simulations, data wrangling, and EDA. Supervised learning deals with prediction problems where the outcome variable is known such as predicting a price of a house in a certain neighborhood or an outcome of a congressional race. The section on unsupervised learning is focused on problems where the outcome variable is not known and the goal of the analysis is to find hidden structure in data such as different market segments from buying patterns or human population structure from genetics data. |
| Web Site | Vergil |
| Department | Quantitative Methods/Social Sciences |
| Enrollment | 10 students (50 max) as of 5:06PM Saturday, May 10, 2025 |
| Subject | Quantitative Methods: Social Sciences |
| Number | GR5058 |
| Section | 001 |
| Division | Graduate School of Arts and Sciences |
| Campus | Morningside |
| Note | PRIORITY QMSS STUDENTS |
| Section key | 20233QMSS5058G001 |
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