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
|
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.
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.
