MSDS Curriculum

Curriculum

At Boston University’s Faculty of Computing & Data Sciences (CDS), the MS in Data Science (MSDS) program equips students with advanced analytical, computational, and problem-solving skills. Grounded in real-world application and interdisciplinary collaboration, the curriculum prepares graduates to lead in data science, AI, and emerging technology fields.

The 32-credit program is designed with flexibility in mind, allowing students to pursue academic or professional career paths and complete the degree in as little as nine months (two semesters). Students choose between a Core Methods Focused Concentration and an Applied Methods Focused Concentration, tailoring their studies to their interests and goals. In addition to core and concentration coursework, the program offers the option to extend learning through a summer internship or a master’s thesis course—enabling completion over 16 months. Please note that the summer internship course is only available to students completing the program in 16 months. All students begin the program in September; a spring entry term is not offered.

Requirements

Eight semester courses (32 credits) approved for graduate study are required.

Course requirements include 5 competency courses, with at least one in each of the following areas:

  • A1 Modeling and Predictive Analytics
  • A2 Data-Centric Computing
  • A3 Machine Learning and AI
  • A4 Social Impact
  • A5 Security and Privacy

Plus 3 additional courses:

  • CDS DS 701 Tools for Data Science (Must be taken in the Fall Semester)
  • Concentration Elective 1
  • Concentration Elective 2

CDS DS 701: Tools for Data Science

The goal of the course is to give students exposure to, and practical experience in, formulating data science questions – particularly learning how to ask good questions in a specific domain.  The course covers methods of obtaining data and common methods of processing data from a practical standpoint. It is organized around a semester-long group project in which students are placed into teams and engage with “clients” who bring data science questions from a particular domain.

Competency Courses

Below is only a sample list of courses. The actual course list varies each semester. Once enrolled, students will receive an updated list of the available courses that semester.

Core Methods Concentration

One CDS DS 701: Tools for Data Science course plus two courses from any of the following Group A areas (see above):

A1 - Modeling and Predictive Analytics
A2 - Data-Centric Computing
A3 - Machine Learning and AI

Applied Methods Concentration

One CDS DS 701: Tools for Data Science plus two courses from the approved list of applied methods courses (all courses below are 4 credits unless otherwise noted). Students are free to take any two courses from the entire list below.   Some courses naturally form pathways but pathways are not a requirement; students may mix and match across applied areas.