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.
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