Master's in Data Science
WPI is one of only a handful of universities offering a master's in data science that prepares graduates to work in the rapidly expanding field.
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 with bachelor's degrees in computer science, mathematics, business, engineering and quantitative sciences would typically qualify, if they meet the above background requirements. 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.
Curriculum for a Master's Degree in Data Science
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
Plan of Study (30 credits)
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.
Antoine Crews
Hometown: Detroit, Michigan
Major: Mechanical Engineering
Current Employer: Pratt & Whitney, Mechanical Engineer-Design. I love that I use technical information learned in the classroom to create real-world products.
Why WPI? I chose WPI because of the projects that could elevate my professional career. During my tenor at WPI, I got the chance to travel and work with incredible professors.
Contributions to systems and control theory by Professor Kazantzis are recognized by the scientific community
The work of Professor Nikolaos K. Kazantzis on nonlinear state estimation and observer design has been recognized and will be presented as a tutorial session by other members of the systems community at the 2022 European Control Conference in London:
ELECTIVES
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.
Research
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
Graduate Studies Series
Learn from our enrollment team members and other guests by attending quick and convenient 30-minute webinars we designed to highlight popular topics when starting grad school. Take a deep dive into specific areas of interest such as how to funding, how to ace your application, student services, and more!
Getting Involved As You Earn Your Data Science MS
Featured Faculty
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.
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 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.
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.
I am Associate Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy professorships in Mathematical Sciences and Data 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.