MS in Statistical Practice
The Master of Science in Statistical Practice is designed for students who want to acquire fundamental training in statistics and how it is applied in fields like economics, education, law, management, science, and social science, to real-world problems. It is suitable for students with backgrounds in fields like biology, bioinformatics, economics, management, neuroscience, psychology, and various areas of engineering.
Learning Outcomes
The learning outcomes for the MS in Statistical Practice (MSSP) program are three-fold:
- Based on information provided by clients, distill research questions, propose experimental designs, and suggest time and resource requirements.
- Respond to a research question by designing a program of data collection, organization, analysis, and modeling that addresses the research questions.
- Defend choices made in program design by comparing selected methodologies with alternatives.
- Explain how decisions and actions that are taken in statistical practice relate to probability and statistical principles and theory.
Course Requirements
In order to complete the Master of Science in Statistical Practice, students must successfully complete eight one-semester courses (32 credits). The specific course requirements are as follows:
- GRS MA 675 Statistics Practicum 1
- GRS MA 676 Statistics Practicum 2
- GRS MA 677 Conceptual Foundations of Statistics
- GRS MA 678 Applied Statistical Modeling
- GRS MA 679 Applied Statistical Machine Learning
- 3 Statistics or Data Science related electives at the level of 500 or above approved by the advisor, excluding GRS MA 681, 684, and 685
Language Requirement
There is no foreign language requirement for this degree.
Final Portfolio
In addition to the course requirements above, students must successfully complete a final portfolio containing consulting and project work that they have carried out during their tenure in the program.
