The university continues to monitor the circumstances related to the pandemic. Spring 2021 course offerings are set. However, the course delivery methods and locations are still being updated and will be finalized in the Schedule of Classes by December 4, 2020.
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Courses - Summer 2020
SURV
Survey Methodology Department Site
SURV665
Introduction to Real World Data Management
Credits: 2
Grad Meth: Reg, Aud
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Data is omnipresent in the contemporary world coming in different shapes and sized: from survey data to found data. In order to make use of such data through analysis it is necessary first to import and clean it. This is often one of the most time consuming and difficult parts of data analysis. In this course you will learn both the conceptual steps needed in preparing data for analysis as well as the practical skills to do this. The course will cover all the essential skills needed to prepare data be it survey data, administrative data or found data.
SURV667
Introduction to Record Linkage with Big Data Applications
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: Basic statistical concepts; and intermediate knowledge of R.
Recommended: Familiarity with regular expressions, the R packages ggplot2 and tidyverse.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Methods to combine data on given entities (people, households, firms etc.) that are stored in different data sources. By showing the strengths of these methods and by showing how each of them are performed in practice using R, the course will demonstrate the various benefits of record linkage. Participants will also learn about potential challenges that record linkage projects may face.
SURV673
Introduction to Python and SQL
Credits: 1
Grad Meth: Reg, Aud
Recommended: Background knowledge in programming in Python and SQL structures.
Basics of Python and SQL for data analysis.Students will explore real publicly-available datasets, using the data analysis toolsin Python to create summaries and generate visualizations. Students will learn thebasics of database management and organization, as well as learn how to code inSQL and work with PostgreSQL databases. By the end of the class, students shouldunderstand how to read in data from CSV files or from the internet and becomfortable using either SQL or Python to aggregate, summarize, describe, andvisualize these datasets.
SURV699M
Special Topics in Survey Methodology; Review of Statistical Concepts
Credits: 3
Grad Meth: Reg, Aud
SURV699Y
Special Topics in Survey Methodology; Modern Workflows in Data Science
Credits: 2
Grad Meth: Reg, Aud
SURV702
Analysis of Complex Survey Data
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: One or more graduate courses in statistics covering techniques through OLS and logistic regression, a course in applied sampling methods.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
The development and handling of selection and other compensatory weights for survey data analysis; the effects of stratification and clustering on survey estimation and inference; alternative variance estimation procedures for estimated survey statistics; methods and computer software that take into account the effects of complex sample designs on survey estimation and inference; and methods for handling missing data, including weighting adjustment and imputation.
SURV736
Introduction to Web Scraping with R
Credits: 1
Grad Meth: Reg, Aud
Prerequisite: Students are expected to be familiar with the statistical software R.
Recommended: Knowledge about the tidyverse packages, in particular, dplyr, plyr, magrittr, and stringr.
Restriction: Permission of BSOS-Joint Program in Survey Methodology department.
Provides a condensed overview of web technologies and techniques to collect data from the web in an automated way. To this end, students will use the statistical software R. The course introduces fundamental parts of web architecture and data transmission on the web. Furthermore, students will learn how to scrape content from static and dynamic web pages and connect to APIs from popular web services. Finally, practical and ethical issues of web data collection are discussed.
SURV753
Machine Learning II
Credits: 2
Grad Meth: Reg, Aud
Prerequisite: SURV751; or comparable knowledge or experience.
Recommended: Familiarity with the statistical programming language R.
Social scientists and survey researchers are confronted with an increasing number of new data sources such as apps and sensors that often result in (para)data structures that are difficult to handle with traditional modeling methods. At the same time, advances in the field of machine learning (ML) have created an array of flexible methods and tools that can be used to tackle a variety of modeling problems. Against this background, this course discusses advanced ML concepts such as cross validation, class imbalance, Boosting and Stacking as well as key approaches for facilitating model tuning and performing feature selection. In this course we also introduce additional machine learning methods including Support Vector Machines, Extra-Trees and LASSO among others. The course aims to illustrate these concepts, methods and approaches from a social science perspective. Furthermore, the course covers techniques for extracting patterns from unstructured data as well as interpreting and presenting results from machine learning algorithms. Code examples will be provided using the statistical programming language R.
Students must have access to a standalone web camera and a headset to participate in the weekly online meetings.