students in class overlooking laptops
Data Science, MS

Program Overview

The Master of Science in Data Science (MSDS) is a full-time, one-year program housed at USF’s downtown San Francisco campus. The 35-unit program begins in early July every year and features a modern, open-source-focused curriculum for students who seek the technical expertise required to become data scientists and analysts, and the business skills to apply this knowledge effectively and strategically.

Data Science Boot Camp

The program begins with our boot camp, an intensive review of the foundational knowledge and skills required for success in the MSDS program. Students complete accelerated review courses in probability & statistics and computation for analytics and also must pass a linear algebra competency exam in order to move forward in the program. Additionally, all students take a course in exploratory data analysis & visualization for a total of three courses and one linear algebra exam in the introductory summer semester.

Nine Month Practicum

Practicum projects allow students to work for 15 hours per week for nine months tackling data science problems at organizations in the San Francisco Bay Area and beyond. All students are guaranteed a practicum placement.


Partnership with Leading Cloud Computing Providers

Our partnership provides students with significant support in the form of credits and trainings to gain experience across a variety of technologies. Select student projects may also be featured on the providers' web pages. Our partnership includes top cloud computing providers Amazon Web Services (AWS) Educate, Google Cloud Platform (GCP) for Education, Databricks University Alliance, and MongoDB for Academia.

Example Project

Class Schedule

Classes are held Monday through Friday during the day, with four classes running simultaneously each module (half a semester). Two days per week are devoted to practicum work, which begins in mid-October. Students take classes together as a cohort and most classes are split into two sections to keep class sizes small.

    • Application Development
    • Machine Learning
    • Statistical Modeling
    • Natural Language Processing
    • Business Strategy
    • Design of Experiments
    • Distributed Computing
    • Deep Learning
    • Data Visualization
    • Business Communication