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 | ||
| SURV400 | Fundamentals of Survey and Data Science | 3 |
| 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 Credits | 30 | |