USF’s one-year Master of Science in Analytics (MSAN) program delivers a rigorous curriculum focused on mathematical and computational techniques in the emerging field of data science. The curriculum emphasizes the careful formulation of business problems, selecting effective analytical techniques to address those problems and communicating solutions in a clear and creative fashion.
98% of MSAN students are employed within three months of graduation at companies such as Google, Williams-Sonoma, Amazon, Capital One Labs, Eventbrite, and Mozilla.
A Technically Challenging Curriculum
The program's challenging curriculum features seven-week courses designed specifically for MSAN students — they're not offered in other programs or departments. Students master subjects from computer science, statistics, and management such as regression, web scraping, SQL and NoSQL database management, natural language processing, business communications, machine learning, cluster analysis, application development, and interviewing skills. Students primarily use programming languages like R and Python in their classes and learn how to effectively use distributed computing technology such as MapReduce, Hadoop, and Spark, and become intimately familiar with cloud technology such as Amazon Web Services.
Practicum projects allow students to work an average of 15 hours per week for nine months tackling data science and analytics problems at companies around the San Francisco Bay Area and beyond. Past and current partners include Uber, Airbnb, Eventbrite, Google, Capital One Labs, AT&T Big Data, Zephyr Health, and the Houston Astros. Groups of two to four students - supervised by MSAN faculty - work on a data-driven business problem and produce a defined set of deliverables.
Our faculty represent the fundamental multidisciplinary nature of the big data industry. They’re traditional academics and data scientists actively working in the field, using real industry experience to inspire their instruction. Their areas of expertise include deep learning, natural language processing, databases, statistical modeling, network analytics, algorithms, unsupervised learning, machine learning, optimization, health analytics and signal processing.