MSDS students choose among the many introductory graduate courses offered to students in the PhD program. These courses cover areas of computer science, optimization, linear algebra and statistics for students that have not had prior exposure to this required course work. Master’s students are fully integrated in the academic activities of the department alongside the PhD students.
Students must complete the required 5 core courses, 4 electives, and a final project to complete the program. There are also three foundational courses that students can test out. For the students who test out of foundational courses, the minimum number of courses taken in the program is 9. For the students who take all foundational courses, it is 12. These foundational courses can be taken in the summer before the program starts. Finally, students will be able to engage in a variety of opportunities across the Data Science Institute research programs and partnerships during their residency in the program.
Interested students will have the opportunity to test out of each of the 3 foundation courses below. Each of the courses will be offered in the late summer and offered online before the start of the fall quarter.
- Computational Foundations for Data Science
- Mathematical Foundations for Data Science
- Statistical Foundations for Data Science
- Introduction to Data Science
- Systems for Data and Computers/Data Design
- Data Interaction
- Introduction to ML and AI or Foundations of Machine Learning and AI Part I
- Responsible Use of Data and Algorithms
Four graduate-level electives can be selected from a wide variety of courses in Data Science, Computer Science, Statistics and across the University.
The online application portal will begin accepting applications for Fall 2024 admission in early Fall 2023. To ensure full consideration, applicants should apply by the deadline. The program may accept applications after the deadline if the cohort is not filled.