Fall 2024 Quantitative Methods: Social Sciences GR5058 section 001

DATA MINING FOR SOCIAL SCIENCE

DATA MINING FOR SOCIAL SC

Call Number 10978
Points 4
Grading Mode Standard
Approvals Required None
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 8 students (50 max) as of 4:05PM Wednesday, July 24, 2024
Subject Quantitative Methods: Social Sciences
Number GR5058
Section 001
Division Graduate School of Arts and Sciences
Campus Morningside
Note PRIORITY QMSS STUDENTS
Section key 20243QMSS5058G001