Survey and Data Science, Master of Professional Studies (M.P.S.)

Non-thesis only: 30 credits required

The online International Master of Professional Studies in Survey and Data Science will provide post-baccalaureate training for individuals interested in broadening their knowledge and understanding of the emerging field of data science, the conduct of sample surveys, practical applications of data analysis and survey methodology, and data management, along with the skills needed to communicate results.
 
Survey methodology, which is already an interdisciplinary field drawing upon statistics, sociology, economics, political science, informatics, public health (e.g., physical measures taken on respondents), and the geographic sciences (e.g., geographic information systems), is now intersecting with the big data world. As public and private organizations are increasingly combining various data sources, including survey data, for the purpose of decision making, the need for professional development in data generation, quality and analysis is on the rise. The online environment is convenient for working professionals who cannot easily travel to a traditional campus. In addition, courses will be shared with our international partners, providing a rich perspective to class discussions.

Course Title Credits
Required course
SURV400Fundamentals of Survey and Data Science3
Data Generating Processes (choose 4 credits). Acceptable courses include the following:4
Sampling Theory
Sampling
Experimental Design and Causal Inference
Questionnaire Design
Usability Testing for Survey Research
or SURV699A
Sampling II
Web Survey Methodology
or SURV699Q
Introduction to Record Linkage with Big Data Applications
Introduction to Python and SQL
Introduction to Web Scraping with R
Data Curation and Storage (choose one course). Acceptable courses include the following:3
Introduction to Real World Data Management
Introduction to Record Linkage with Big Data Applications
Modern Workflows in Data Science
or SURV699Y
Item Nonresponse and Imputation
Multiple Imputation
Step by Step Survey Weighting
Principles of Digital Curation
Database Design
Big Data Infrastructure
Data Analysis (choose 6 credits). Acceptable courses include the following:6
Review of Statistical Concepts
or SURV699M
Experimental Design and Causal Inference
An Introduction to Small Area Estimation Methods (An Introduction to Small Area Estimation Methods)
Introduction to Python and SQL
Analysis of Complex Survey Data
General Linear Models
Item Nonresponse and Imputation
Multiple Imputation
Inference from Complex Surveys
Step by Step Survey Weighting
Introduction to Big Data and Machine Learning
Machine Learning II
or SURV699K
Data Output/Access (choose 3 credits). Acceptable courses include the following:3
Ethical Considerations for Data Science Research
or SURV699A
Privacy Law
or SURV699C
Modern Workflows in Data Science
or SURV699Y
Data Privacy and Data Confidentiality
Introduction to Data Visualization
Information Ethics
Data Visualization
Electives. Acceptable courses include any 600-level or 700-level SURV courses.11
Total Credits30