Data Science Online (MS)

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

Graduate Course Catalog

How Online Learning Works

Admissions for Online Programs

Student Support

Tuition & Financial Aid

Meet the Team

eBook: Data Science, In Every Industry

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 2017

DS 501. Introduction to Data Science
CS 573. Data Visualization
MIS 584. Business Intelligence

EARLY SPRING TERM 2018

DS 501. Introduction to Data Science
DS 502. Statistical Methods for Data Science
CS 542. Database Management Systems
OIE 541. Operations Risk Management or
MIS 576. Project Management

LATE SPRING TERM 2018

DS 503. Big Data Management
CS 548. Knowledge Discovery and Data Mining
OIE 541. Operations Risk Management or
MIS 576. Project Management

SUMMER TERM 2018

DS 501. Introduction to Data Science
CS 5007. Intro to Applications of CS w Data Structures & Algorithms
MA 511. Applied Statistics for Engineers and Scientists
OIE 598. Optimization Methods for Business Analytics

FALL TERM 2018

DS 501. Introduction to Data Science
DS 504. Big Data Analytics
CS 542. Database Management Systems
CS 573. Data Visualization or
MIS 584. Business Intelligence

EARLY SPRING TERM 2019

DS 501. Introduction to Data Science
DS 502. Statistical Methods for Data Science
DS 503. Big Data Management
CS 548. Knowledge Discovery and Data Mining
OIE 541. Operations Risk Management or
MIS 576. Project Management

LATE SPRING TERM 2019

DS 502. Statistical Methods for Data Science
DS 503. Big Data Management
CS 573. Data Visualization or
MIS 584. Business Intelligence
CS 542. Database Management Systems or
CS 548. Knowledge Discovery and Data Mining
OIE 541. Operations Risk Management or
MIS 576. Project Management

SUMMER TERM 2019

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
Technical Elective and/or Business Elective

TECHNICAL & BUSINESS ELECTIVES

(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

Technical Electives
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	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
Business Electives
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 Additional electives are offered on-campus.

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.

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.

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.

Career Outlook

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Data Science Department