In a world that relies more and more on the collection and analysis of data to derive business value, an MS in Data Science from WPI is your foot in the door to careers in any industry.  Beginning in 2017, you can earn an MS in Data Science online, making it possible to advance your education wherever you live.

Our convenient online format is not the only benefit; we offer paths of study in Data Science that are tailored to your aspirations. In addition to the core courses that teach data-science essentials, you’ll choose from a variety of electives that will prepare you for a future in data science.

WPI also offers an Online Graduate Certificate in Data Science.

Program Highlights

By the time you earn your MS in Data Science, you will have mastered:

  • 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

Interested applicants should have a working knowledge of statistics, mathematics, and basic programming in at least one language.

This degree is also offered on campus.
Learn more

Plan of Study (33 credits)

In order to earn a MS in Data Science, you are required to take DS 501—Introduction to Data Science—core coursework in four areas, and electives, totaling 30 credits.

Core areas of study:

  • Data Analytics and Mining 
  • Data Access and Management 
  • Mathematical Analytics 
  • Business Intelligence and Case Studies 

In addition, students will complete a three-credit graduate qualifying project (GQP). This practicum provides you with a strong capstone experience in which to integrate theory and practice as you apply your data science and analytics skills.

Curriculum Overview

I.  Core requirements in the 5 categories as detailed below – 15 credits / 5 courses

II.  Electives – 15 credits / 5 courses

III.  Graduate Qualifying Project – 3 credits

TOTAL 33 credits

NOTE: All curriculum plans 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

Students in the online M.S. Program must include appropriate course selections from the following five categories and complete the credit requirement with electives:

  • Integrative Data Science  (Required)
  • Mathematical Analytics (3 credits)
  • Data Access and Management (3 credits)
  • Data Analytics and Mining (3 credits)
  • Business Intelligence and Case Studies (3 credits)
  • Electives (5 courses / 15 credits) 

Course Schedule

FALL TERM

  • DS 501. Introduction to Data Science
  • DS 504. Big Data Analytics
  • CS 542. Database Management Systems
  • CS 573. Data Visualization
  • ECE 502. Analysis of Probabilistic Signals and Systems
  • BUS 546. Managing Technological Innovation
  • FIN 500. Financial Information and Management

  • OIE 542. Risk Management and Decision Making

EARLY SPRING TERM

  • DS 501. Introduction to Data Science
  • DS 503. Big Data Management
  • MIS 576. Project Management
  • OBC 505. Teaming and Organizing for Innovation 

LATE SPRING TERM

  • DS 502. Statistical Methods for Data Science
  • CS 548. Knowledge Discovery and Data Mining
  • MIS 584.  Business Intelligence 
  • OIE 542. Risk Management and Decision Making 
  • OIE 598.  Engineering Economics 

SUMMER TERM

  • DS 501. Introduction to Data Science
  • CS 5007. Intro to Applications of CS with Data Structures and Algorithms
  • MA 511. Applied Statistics for Engineers and Scientists
  • ECE 504. Analysis of Deterministic Signals and Systems
  • ECE 506. Introduction to Local and Wide Area Networks

A complete listing of all courses applicable to the Data Science program may be found in the WPI Graduate Catalog under Data Science. Please note that only the courses shown above are offered in online sections.  Additional electives are offered on-campus.

Course Offerings are subject to change.  If you are interested in enrolling, please contact us for the most up-to-date information.

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Technological advances in devices, software, networking, and other technologies have given rise to digital data rich in variety, volume, velocity, and complexity.
Elke Rundensteiner
Professor, Computer Science
Director of the Data Science Graduate Program

Admissions 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. Remedial courses at the undergraduate level would not count towards meeting the M.S. degree requirements. The determination of what course or courses will satisfy this provision will be made by the DS Steering Committee, which consists of faculty members from the participating departments at WPI. Students applying to the certificate in Data Science are expected to meet the same qualifications described above.

After Graduation

As a graduate from WPI’s Data Science Program, you have the prestige, the skills and the solid education to tackle any career path you choose.

Webinar: Open House

View our on-demand webinar to be introduced to our Data Science online graduate program. Get an overview of program logistics, what online learning looks like, the application process, and more!