The Michigan Program in Survey & Data Science (MPSDS) at the University of Michigan prepares students for advanced work at the intersection of social research, survey methodology, and modern data science. The MS program gives students a deep grounding in both theory and practice—from survey design and collecting data to analyzing diverse data sources while ensuring data quality. Opportunities for part-time study can be discussed with program staff. All MS students must complete the following requirements:
- 46 credit hours of approved core and elective coursework requirements for survey statistics, social science or data science area of concentration
- Approved practical work experience
- Attendance at 75% of scheduled department seminar series and professional development events
- Adherence to Rackham Graduate School Academic Policies and departmental policies and procedures.
These degree sequences are illustrative and do not define all programs of study. Students will, with the consent of their advisors, be given the flexibility to depart from these course sequences to meet particular training needs. For example, students may craft, through approved cognate and electives, a program of study that emphasizes business applications of survey methods, particularly in marketing and market research. Advisors will work with students and faculty in the School of Business Administration to find courses that will allow the student to acquire marketing or market research training to complement the survey methodology training offered by the program.
The statistical science track is designed for students who wish to specialize in more theoretical aspects of statistics in survey and data science, such as sample design, inference with complex samples, post-survey adjustments, and statistical methods dealing with missing data.
The social science track is designed for students who wish to specialize in more measurement-related aspects of survey and data science, such as questionnaire design, data collection methods, cognitive psychological insights into survey measurement, and efforts to reduce various nonsampling errors in data collection.
The data science track is designed for students who wish to specialize in the more computational aspects of survey and data science, such as research involving non-traditional organic and/or messy data, data visualization, and machine learning algorithms