Minor in Data Science

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Program Mission

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The minor in Data Science introduces students to the conceptual framework of data science. The pillars of that framework include analytical, systems, design and value components that uniquely combine to form the field of data sciences.  This minor prepares students for careers or post­graduate work that necessitate exposure to data science techniques. Emphasis on responsible data science is included as a core principle of the program.

Data science is new and powerful in many ways but also builds on a foundation  provided by computational and quantitatively oriented fields.  Students in the minor receive the education needed to appreciate these new developments in the context of their theoretical heritage and practical application.

The minor was developed to leverage the established structure of the university in order to meet the requirements of what is an intrinsically interdisciplinary, dynamic, and externally engaged field. The traditional structure of higher education requires such enhancement—the feasibility of which has been established by the MSDS—in order to remain relevant to its students and to serve the Commonwealth and beyond.

 

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Program Description

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Prerequisite course (One of below):

• STAT 1100 - Chance: An Introduction to Statistics - Credits: 3   
• STAT 1601 - Introduction to Data Science with R - Credits: 3   
• STAT 1602 - Introduction to Data Science with Python - Credits: 3   
• STAT 2120 - Introduction to Statistical Analysis - Credits: 4    
• STAT 2020 - Statistics for Biologists - Credits: 4   
• APMA 3100 - Probability - Credits: 3    
• APMA 3110 - Applied Statistics and Probability - Credits: 3   
• APMA 3120 - Statistics - Credits: 3   

Also, courses that satisfy the “Computation, quantitative and data analysis” in the New College curriculum requirement will be accepted 

Other courses may be proposed, subject to approval by the Program Director.

Minimum grade: C or higher

 

Minor in Data Science Requirements 

Foundational Programming Course (Select one course)

• DS 2001 - Programming for Data Science - Credits: 3   (new)
• CS 1110 - Introduction to Programming - Credits: 3   
• CS 1111 - Introduction to Programming - Credits: 3   
• CS 1112 - Introduction to Programming - Credits: 3   
• CS 2110 - Software Development Methods - Credits: 3   

 

Analytics Course (Select one course)

• DS 3001 - Practice of Data Science - Credits: 3     
• STAT 3080 - From Data to Knowledge - Credits: 3     

 

Systems Course (Select one course)

• DS 3002 - Data Science Systems - Credits: 3   
• CS 4750 - Database Systems - Credits: 3   
• COMM 3220 - Database Management Systems and Business Intelligence - Credits: 3  (for undergrad commerce majors)
• SYS 2202 - Data and Information Engineering - Credits: 3  (Systems majors only)

 

Data Design or Value Course (Select one course or suggest another course with Program Director approval)

Recommended courses:

• DS 3003 - Communicating with Data - Credits: 3  (new)
• STS 2500 - Science and Technology in Social and Global Context - Credits: 3  

SARC 5400: Data Visualization - Credits: 3

The below courses are also options: 

• COMM 3810 - Business Ethics - Credits: 3    
• LPPP 4210 - Integrating Ethics in Public Policy - Credits: 3   
• LPPS 5360 - Imagining Equitable Policy - Credits: 3
• NASC 4200 - Leadership and Ethics - Credits: 3   
• NUIP 3311 - Research, Ethics, Advocacy, and Leadership: Quality, Safety, and EBP - Credits: 3   
• NUIP 4311 - Research, Ethics, Advocacy, and Leadership - Credits: 2    
• STS 4600 - The Engineer, Ethics, and Professional Responsibility - Credits: 3  

 

Domain Elective (Select one course) Or Final Project as outlined below

Students can select a data-driven course from a domain area of interest with approval by the Program Director.

 

Final Project 

DS 4002 Final Project (DS course) - Students can complete an applied data science project.  This project can take the shape of an approved project plan that students submit or by a project provided by the School of Data Science.  Students will be placed in groups and will present their findings to the SDS and interested faculty at the end of the semester. There will be a special focus on the ethics of the project to ensure full alignment with the School of Data Science defined model of data science. All other requirements of the minor must be completed before enrollment (via instructor permission) into this course.

 

The completion of Data Science minor requires a minimum of 15 credits. A student may double-count no more than 2 courses between this minor and another program (major/minor).

 

Students may transfer up to 1 course, whether it be post or pre-matriculation. This limit is also included in the double-counting two course limit. For example, in the absence of a double-counting limit from the School of Enrollment, a student may transfer 1 course, and double-count a second course, thus meeting the 2-course limit.