WPI is one of only a handful of universities that prepares graduates to work in the rapidly expanding field of Data Science.

In our pioneering MS in Data Science program, you’ll work closely with faculty and peers to synthesize huge amounts of digital information from multiple sources. You’ll derive new insights and articulate these findings into innovative solutions for how we live, work, and interact with the world around us. Your expertise will be in high demand in the workplace, as you tackle some of the world’s greatest challenges.

Admission Requirements
Students applying to the M.S. Degree program in Data Science (DS) are expected to have a bachelor's degree with a strong quantitative and computational background including coursework in programming, data structures, algorithms, calculus, linear algebra and introductory statistics. Students with bachelor's degrees in computer science, mathematics,  business, engineering and quantitative sciences would typically qualify, if they meet the above background requirements. A strong applicant who is missing background coursework may be provisionally admitted, with the expectation that he or she will take and pass one or more courses in this area of deficiency either during the summer prior to admission or within the first semester after admission.

Applying to the Data Science MS Program
Students will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.

data science


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

  1. Core requirements in the 5 categories as detailed below – 15 credits / 5 courses
  2. Graduate Qualifying Project – 3 credits (DS598)  or   MS thesis (DS 599) -  9 credits.
  3. 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


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


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.

This degree is also offered online.


Since traditional data processing applications can no longer handle today’s large and complex datasets, WPI Data Science researchers are innovating new models and solutions. As a graduate student, you’ll work alongside faculty who are fueling breakthroughs that have direct, real-world impact in health, genetic analysis, sustainability, educational software, financial trading, and more. 

Whether your research interest is using predictive analytics to identify cyber threats or empowering “smart” cities to make data-driven policy changes critical for societal well being, WPI has the capacity to help you design and complete your master’s degree in the burgeoning field of data science.

Software Tools & Labs

The Data Science Innovation Lab is dedicated workspace for project work by students in the Data Science program. Robust servers and computer clusters are available for experimenting with large-scale datasets throughout labs at WPI, including many interdisciplinary facilities.

State-of-the-art software programs offered:

Faculty Profiles

Featured Faculty

Carolina Ruiz

Carolina Ruiz

Associate Professor
Computer Science

Carolina Ruiz's research interests are in data mining, machine learning, and artificial intelligence. Together with her graduate and undergraduate students, colleagues in computer science and biology, and medical doctors, Ruiz investigates and develops data mining algorithms for genomics and for clinical medicine. In addition to being a faculty member in computer science, she is a founder and active member of the bioinformatics and computational biology program at WPI.

Andrew C. Trapp

Andrew Trapp

Associate Professor
Foisie Business School

Decision-making is becoming increasingly complex as data expands and resources decrease. My research centers on using prescriptive (integer optimization) and predictive (machine learning) analytics, together with algorithms, to effectively allocate scarce resources. My work employs mathematical modeling and the development of methods and tools to benefit vulnerable and marginalized individuals, groups, and populations.

Jian Zou

Jian Zou

Associate Professor
Mathematical Sciences

Professor Zou's research focuses on financial time series and spatial statistics with applications to epidemiology, public health and climate change. His most recent research on statistical theory and methodology addressed a wide range of challenges including high dimensionality, complex dependencies, and space and time variations. His research in high-frequency financial data tackled problems with high dimensionality, which is currently a hot topic in statistics.

Mohamed Y. Eltabakh

Mohamed Eltabakh

Associate Professor
Computer Science

Professor Eltabakh’s research is in the broad area of Database Management Systems and Information Management. In particular, his work is in the areas of query processing and optimization, indexing techniques, scientific data management, and large-scale data analytics. Prof. Eltabakh is currently exploring possible extensions to both database management systems and Hadoop framework to support scientific applications and health-care systems. He is a member of the Database Systems Research Group (DSRG) and a faculty member of the Bioinformatics and Computational Biology (BCB) program.


Getting Involved

Getting Involved

We’re data scientists – we use tools of the trade and big data analytics to innovate. Follow department happenings and industry trends via our social media channels on Facebook and LinkedIn.

Students walking around the fountain in the springtime

After Graduation