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.
WPI is one of only a handful of universities offering a master's in data sciences that prepares graduates to work in the rapidly expanding field.
In our pioneering data science master's degree program, you’ll work closely with faculty and peers to synthesize huge amounts of digital information from multiple sources. With our master's degree 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 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 master's degree 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 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 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 Foisie Business School coursework within the master's degree in data science program.
Core Data Science Coursework Requirement (15 Credits / 5 Courses)
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)
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 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 33 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.
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.
Software Tools & Labs
The Data Science Innovation Lab is dedicated workspace for project work by students in the Data Science master's 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:
Get Ready For Your Data Science Career After Graduation
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 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.
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.
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.
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.
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.