The MS in Data Science (MSDS) in Computing & Data Sciences at Boston University prepares you to make significant contributions to all aspects of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse. It is our goal that this program leads to solution of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.
The MSDS is a flexible program designed to meet the goals of students looking to pursue either academic or professional careers in Data Science. Upon completion of the program, students will be prepared to pursue careers in which they will become leaders in their chosen areas, whether in academia through advanced graduate work in a PhD, or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).
The MSDS is a 32-credit flexible program designed to meet the goals of students looking to pursue either academic or professional careers in data science, and can be completed in as little as 9 months. Students will declare either a Core Methods Focused Concentration or Applied Methods Focused Concentration. In addition to the core curriculum and concentration courses, the MSDS program offers students a unique opportunity to enhance their learning through an optional summer internship or master's thesis course. As a result, the program can be extended and completed over 12 or 16 months. All students begin the program once every year in September, Spring entry term is not offered.
Learning Outcomes
Mastery of the principal tools of data decision making, including defining models, learning model parameters, management, and analysis of massive datasets, and making predictions.
Demonstrated competence in application of data science tools to address substantive questions in one or more applied areas, and will address those questions through sophisticated use of data science tools, including tools specifically appropriate for each applied area.
Ability to extend tools of data decision making, including building specialized computational pipelines, automating data workflows, and developing human-computer interfaces.
