Minor in Data Science:


The ability to extract useful information from large volumes of data is becoming increasingly important in many disciplines.  The Minor in Data Science is thus designed to provide WPI undergraduates in any major with the tools essential to understand and work with data by applying models, algorithms and statistical techniques to data.  The minor complements many of the existing undergraduate majors at WPI from sciences to engineering that increasingly must work with large digital data sets using computational and statistical techniques and tools by providing these students with the core competencies of Data Science.  


The Minor in Data Science consists of 2 units, all of which must be selected from the list of approved Data Science minor courses.  These 2 units must be selected to include the following:

  • Three courses, one from each of the three areas (Business, Computer Science, Mathematical Sciences) at the 2000 level or above from the list of approved Data Science minor courses
  • Two courses at the 3000 level or above, as follows:
    •  DS 3001 Foundations of Data Science
    •  Any other 3000 level or above course from the list of approved Data Science minor courses
  • One course at any level selected from the list of approved Data Science minor courses  

The Minor in Data Science is open to all undergraduate majors at WPI.  Students majoring in Business, Computer Science, or Mathematical Sciences should consult WPI rules on minors for double-counting courses.  

List of Approved Courses for the Data Science Minor

Any graduate course approved for the Data Science Graduate Program can also be counted towards the Data Science minor. These courses are not repeated here.      

Data Science courses:

  • DS 3001 Foundations of Data Science

Business courses:      

  • BUS 2080 Data Analysis for Decision Making      
  • MIS 3720 Business Data Management       
  • MKT 3650 Consumer Behavior       
  • OIE 3420 Quality Planning: Design and Control       
  • OIE 3460 Simulation Modeling and Analysis       
  • ACC 4200 Managing Performance: Internal and Inter-Organizational Perspectives       
  • OIE 4420 Practical Optimization: Methods and Applications

Computer Science courses:       

  • CS 1004 Introduction to Programming for Non-Majors       
  • CS 1101 Introduction to Program Design*       
  • CS 1102 Accelerated Introduction to Program Design*       
  • CS 2102 Object-Oriented Design Concepts       
  • CS 2119 Application Building with Object-Oriented Concepts       
  • CS 2223 Algorithms      
  • CS 2301 Systems Programming for Non-majors       
  • CS 2303 Systems Programming Concepts       
  • CS 3431 Database Systems I      
  • CS 4120 Analysis of Algorithms       
  • CS 4341 Introduction to Artificial Intelligence       
  • CS 4432 Database Systems II       
  • CS 4445 Data Mining and Knowledge Discovery in Databases       
  • CS 4802 Biovisualization       
  • CS 4803 Biological and Biomedical Database Mining

Mathematical Sciences courses:      

  • MA 2071 Linear Algebra    
  • MA 2611 Applied Statistics I       
  • MA 2612 Applied Statistics II       
  • MA 2621 Probability for Applications†     
  • MA 2631 Probability†       
  • MA 3231 Linear Programming       
  • MA 3627 Introduction to the Design and Analysis of Experiments       
  • MA 3631 Mathematical Statistics       
  • MA 4213 Loss Models – Risk Theory       
  • MA 4214 Loss Models – Survival Models       
  • MA 4235 Mathematical Optimization       
  • MA 4237 Probabilistic Methods in Operations Research       
  • MA 4631 Probability and Mathematical Statistics I       
  • MA 4632 Probability and Mathematical Statistics II 

* Credit may not be earned for both CS 1101 and CS 1102 
† Credit may not be earned for both MA 2621 and MA 2631  

Implementation Date:  This minor is effective 2016-17 academic year onwards.


DS 3001 Foundations of Data Science 

This course provides an introduction to the core ideas in Data Science. It covers a broad range of methodologies for working with and making informed decisions based on real-world data. Core topics introduced in this course include data collection, data management, statistical learning, data mining, data visualization, cloud computing, and business intelligence. Students will acquire experience with big data problems through hands-on projects using real-world data sets.

Recommended background for this course includes statistics knowledge equivalent to MA2611 and MA2612, linear algebra equivalent to MA2071, and the ability to program equivalent to (CS 1004 or CS 1101 or CS 1102) and (CS 2102 or CS 2119).

This course does not fulfill Mathematics, Basic Science or Engineering Science/Design credits.

Implementation Date:  DS 3001 is offered starting 2016-2017 academic year.

Impact on Distribution Requirements: No impact on distribution requirements. This course is part of the new DS Minor being proposed. It is currently not cross-listed with other majors. This may change in the future.