Master's in Data Science

In our pioneering master's in data science, you’ll work closely with faculty and peers to synthesize huge amounts of digital information from multiple sources. With our MS in data science, 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 for the MS in Data Science
Students applying to the MS in data science 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 will typically qualify if they meet the above requirements with bachelor's degrees in the following degree programs: computer science, mathematics, business, engineering, economics, information technology and quantitative sciences, etc. But if you need to complete your bachelor's degree first, check out our BS in data science at WPI. 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 Master's Program
Students will find WPI’s data science master's program degree options listed with engineering, science, and mathematics on the application form.

This program is also available online

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WPI’s data science master's program is interdisciplinary, drawing from Computer Science, Mathematical Sciences, and the 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

To earn a master's degree in data science, you complete a minimum of 30 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 in data science 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.

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 6 to 12 credits

TOTAL 30 credits

NOTE: All plans of study must be approved by the student’s academic advisor.
NOTE: A maximum of 16 credits are allowed from the Business School coursework within the master's degree in data science program.

Students in the MS in Data Science 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)
DS 541. Deep Learning (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. Marketing Analytics
MIS 587. Business Applications in Machine Learning (3 credits)

A complete listing of all courses pre-approved as electives for the MS in 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 30 credits of graduate-level work. The MS in data science curriculum culminates in the Graduate Qualifying Project (GQP), a semester-long team project with an industry partner.

Prefer to pursue your master’s degree in data science online instead? Explore our flexible online MS in data science. Maybe you’re looking for expert proficiency in data science? Our interdisciplinary PhD in data science enables you to gain the leadership edge to elevate your career. In addition to our online MS and PhD, we offer a six-course certificate in data science. This is tailored to students who want to learn how to analyze and interpret data without fulfilling the demands of a data science master’s.

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 data science master's 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.

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Featured Faculty

Elke Rundensteiner

Professor, Computer Science, Program Head, Data Science,

As founding Head 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, and new educational initiatives to our innovative industry-sponsored and mentored Graduate Qualifying projects at the graduate level.

Having served as the primary advisor and mentor of over 43 Ph.D. students who have secured successful professional careers in computing, I'm proud o[...]

Xiangnan Kong

Associate Professor,

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 bio[...]

Yanhua Li

Associate Professor,

Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI). His research interests focus on artificial intelligence (AI) and data science, with applications in smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization. Recently, his research has an emphasis on advancing imitation learning and meta learning in AI for learning and influencing the decision-making strategi[...]

Randy Paffenroth

Associate Professor,

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, o[...]

Andrew Trapp

Professor,

I am Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy appointments in Mathematical Sciences, Data Science, and Computer Science. I hold a Ph.D. in Industrial Engineering from the University of Pittsburgh. My objective is to use science and technology to assist real human need by improving systems that serve vulnerable peoples, such as refugees and asylum seekers, survivors of human trafficking, and children in the foster care system.[...]

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