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Courses - Fall 2019
Survey Methodology Department Site
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Special Topics in Survey Methodology; Reading in Survey Methodology
Credits: 1 - 3
Grad Meth: Reg, Aud
Special Topics in Survey Methodology; Practical Tools II (for Weighting)
Credits: 1
Grad Meth: Reg, Aud
Special Topics in Survey Methodology; Machine Learning for Social Science
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Students should work through one or more R tutorials prior or during the first weeks of class due to the short introduction to R presented in the class. Some resources can be found at the following: https://www.rstudio.com/online-learning/#R, https://cran.r-project.org/manuals.html, or http://www.statmethods.net.

Provides an introduction to supervised statistical learning techniques such as decision trees, random forests and boosting and discusses their potential application in the social sciences. These methods focus on predicting an outcome Y based on some data-driven function f(X) and therefore facilitate new research perspectives in comparison with traditional regression models, which primarily focus on causation. Predictive methods also provide a valuable extension to the empirical social scientists' toolkit as new data sources become more prominent. In addition to introducing supervised learning methods, the course will include practical sessions to exemplify how to tune and evaluate prediction models using the statistical programming language R.
Special Topics in Survey Methodology; Synthetic Population
Credits: 1 - 4
Grad Meth: Reg, Aud
Prerequisite: Mathematical statistics course at the Master's level (e.g., UMD STAT/SURV 420 or equivalent.) If you are unsure about your qualifications for the course, please contact us.
(Perm Req)
Special Topics in Survey Methodology; Applied Data Analytics
Credits: 3
Grad Meth: Reg, Aud
Innovative forms of data never seen before, combined with traditional survey and administrative data, provide a rich resource for data-driven decisions in public policy and the public sector. The program's design offers hands-on training in the context of real challenges facing the sector, by providing direct collaboration among government agencies and students in the program. Skills learned as part of the project work are SQL, Python, Record Linkage, Database Management, use of APIs, Web Scraping, Basic Machine Learning, Text Analysis, Analysis of Network and Geospatial data, Inference, Privacy and Ethics.