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
This degree is also offered online.
  • 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

  • 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


    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.


Turning Big Data into Actionable Information


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.


Close faculty interaction, cutting-edge equipment, and personal attention let you structure your program so it suits your individual career goals. You’ll leave with a degree that will help you succeed in your distinctive path.


Data science research gives you opportunities to work on grand challenge problems with societal importance, including topics such as cybersecurity, healthcare, and sustainability.


Our data science graduate program offers expertise in computer science, statistics, and business topics while giving you essential opportunities to work with industry partners.


WPI’s innovative and multidisciplinary graduate program prepares students to become talented and effective leaders in this rapidly evolving field.

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:

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

Featured Faculty

Elke Rundensteiner

As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.

Fatemeh Emdad

Professor Fatemeh Emdad completed her Ph.D. in Applied Mathematics with a concentration in Applied Mathematics at Colorado State University. She is the recipient of the top-ranked student academic award from the President of Shiraz University. She is the author of the book High Dimensional Data Analysis and more than 20 journal and conference articles. She completed her postdoctoral degree with the University of Texas Medical Branch and Shriners Hospital for Children Burn Unit when she was the NIH postdoctoral scientist fellow (under Ruth L.

Xiangnan Kong

Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.

Yanhua Li

My broad research interests are in analyzing, understanding, and making sense of big data generated from various complex networks in many contexts, including urban network analysis, large-scale network data sampling, measurement, online social behavior modeling. My recent research focuses on exploring the challenges in managing and analyzing big data from urban networks, with an ultimate goal of improving human life quality and designing smarter cities.

Randy Paffenroth

My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.

Andrew Trapp

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.