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, as well as, graduate programs on our Worcester campus

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.

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 (3 credits)
  • Data Access and Management (3 credits)
  • Mathematical Analytics (3 credits)
  • Business Intelligence and Case Studies (3 credits)

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

Core Data Science Coursework Requirement (15 credits / 5 courses)

Students in the online M.S. Program must include appropriate course selections from the following five categories.  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 below are offered in online sections.  Courses listed in the Graduate Catalog that are not listed here are not currently offered online, although courses are continually being added.

 (Note this includes both Core DS courses and electives)

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)

Data Access and Management 3 credits (Select one)

CS 542. Database Management Systems (3 credits) [recommended prerequisite for DS 503]

DS 503. Big Data Management (3 credits)

Data Analytics and Mining 3 credits (Select one)

CS 548. Knowledge Discovery and Data Mining (3 credits) [recommended prerequisite for DS 504]

DS 504. Big Data Analytics (3 credits)

Business Intelligence and Case Studies 3 credits (Select one)

MIS 584. Business Intelligence

ELECTIVES

Students may select 5 courses / 15 credits from the following list.

Please note that only the courses shown below are offered in online sections.  Courses listed in the Graduate Catalog that are not listed here are not currently offered online, although courses are continually being added.  Click here to inquire.

Note the following 4 courses are also listed above in the required core categories.  Any may be used as electives if not selected as a core course.

*indicates recommended electives critical for data scientists

*CS 542. Database Management Systems (3 credits) [recommended prerequisite for DS 503]

*DS 503. Big Data Management (3 credits)

*CS 548. Knowledge Discovery and Data Mining (3 credits) [recommended prerequisite for DS 504]

*DS 504. Big Data Analytics (3 credits)

Note the following two elective courses are designed as ramp up courses for students who may be lacking in sufficient background in either statistics or programming.  If either or both of these are selected they must be completed first, prior to the DS core coursework.

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)

ADDITIONAL ELECTIVES CURRENTLY OFFERED ONLINE
 

(Electives / concentrations offerings are flexible so that the student may choose coursework that applies to their interests and needs)
*indicates recommended electives critical for data scientists

Computer Science courses

CS 5084. Introduction to Algorithms: Design and Analysis

CS 509.  Design of Software Systems

CS 546. Human-Computer Interaction

CS 548. Knowledge Discovery and Data Mining

*CS 573. Data Visualization 

CS 584. Algorithms: Design and Analysis

School of Business courses

ACC 503. Financial Intelligence for Strategic Decision Making

BUS 500. Business Law, Ethics and Social Responsibility

FIN 500. Financial Information and Management

FIN 501. Economics for Managers

MIS 500. Innovating with Information Systems

MIS 573. Systems Design and Development

MIS 576. Project Management

MIS 581. Information Technology Policy and Strategy

OBC 500. Group and Interpersonal Dynamics in Complex Organizations

OBC 501. Interpersonal and Leadership Skills

OIE 500. Analyzing and Designing Operations to Create Value

OIE 541. Operations Risk Management

OIE 544. Supply Chain Analysis and Design

OIE 552. Modeling and Optimizing Processes

OIE 598. Optimization Methods for Business Analytics     

Electrical and Computer Engineering courses

ECE 502. Analysis of Probabilistic Signals and Systems

ECE 503. Digital Signal Processing

ECE 504. Analysis of Deterministic Signals and Systems

ECE 630. Advanced Topics in Signal Processing

Additional electives are offered on-campus.

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.

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

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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.

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