WPI’s Data Science master’s degree program is interdisciplinary, drawing from Computer Science, Mathematical Sciences, and the Robert A. Foisie Business School. This trifecta of disciplines forms the basis for the program’s curriculum that focuses on:
- Database management: extracting and managing data using traditional and cutting-edge methods
- Analysis techniques: machine learning and data-mining algorithms
- A deeper knowledge of statistics and other applied mathematical foundations
- Data-analysis software: a rich diversity of software tools used throughout the program
- Essential management and leadership techniques: business courses provide a holistic approach to data science, developing interpersonal and story-telling skills alongside technical mastery
Plan of Study (33 credits)
To earn a MS in Data Science, you complete a minimum of 33 credits of relevant work at the graduate level, including the core coursework requirements in five areas as described below.
Core areas of study:
- Integrative Data Science (3 credits)
- Data Analytics and Mining (3 credits)
- Data Access and Management (3 credits)
- Mathematical Analytics (3 credits)
- Business Intelligence and Case Studies (3 credits)
The MS curriculum culminates in the three-credit Graduate Qualifying Project (GQP), a semester-long team project with an industry partner, or a nine-credit M.S. thesis.
Curriculum Overview
- Core requirements in the 5 categories as detailed below – 15 credits / 5 courses
- Graduate Qualifying Project – 3 credits (DS598) or MS thesis (DS 599) - 9 credits.
- Electives – between 9 to 15 credits
TOTAL 33 credits
NOTE: All plans of study must be approved by the student’s academic advisor.
NOTE: A maximum of 16 credits are allowed from School of Business coursework within the M.S. DS
CORE DATA SCIENCE COURSEWORK REQUIREMENT (15 CREDITS / 5 COURSES)
Students in the M.S. Program must include appropriate course selections from the following five categories.
Integrative Data Science (Required)
DS 501. Introduction to Data Science (3 credits)
Mathematical Analytics 3 credits (Select one)
DS 502. Statistical Methods for Data Science (3 credits)
MA 542. Regression Analysis (3 credits)
MA 554. Applied Multivariate Analysis (3 credits)
Data Access and Management 3 credits (Select one)
CS 542. Database Management Systems (3 credits)
MIS571. Database Applications Development (3 credits)
DS 503. Big Data Management (3 credits)
CS 561. Advanced Topics in Database Systems (3 credits)
Data Analytics and Mining 3 credits (Select one)
CS 548. Knowledge Discovery and Data Mining (3 credits)
DS 504. Big Data Analytics (3 credits)
CS 539. Machine Learning (3 credits)
Business Intelligence and Case Studies 3 credits (Select one)
MIS 584. Business Intelligence (3 credits)
MKT 568. Data Mining Business Applications
ELECTIVES
A complete listing of all courses pre-approved as electives for the Data Science program may be found in the WPI Graduate Catalog under Data Science.
Two elective courses designed as ramp up courses for students who may be lacking in sufficient background in either statistics or programming, respectively, are listed below. They can count towards the 33 credits of the DS MS degree, However, they cannot be used to meet the above requirements in five core categories.
MA 511. Applied Statistics for Engineers and Scientists
CS 5007. Intro to Applications of CS with Data Structures and Algorithms (Programming for non-CS majors)
Further degree requirements include a minimum of 33 credits of graduate-level work. The MS curriculum culminates in the Graduate Qualifying Project (GQP), a semester-long team project with an industry partner.
