Jan 22, 2022  
Undergraduate Record 2020-2021 
    
Undergraduate Record 2020-2021 [ARCHIVED RECORD]

Minor in Data Science


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Program Mission


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.

 


Program Description


The undergraduate minor in Data Science consists of four required courses and one elective course (or Data Science project course).

In completing the minor in data science students are required to take at least two School of Data Science (DS) courses.

15 credits hours (not including the prerequisite course)

 

Prerequisite course (One of below):

STAT 1100 An Introduction to Statistics

STAT 2020 Statistics for Biologist

STAT 1601 Introduction to Data Science with R

STAT 2120 Introduction to Statistical Analysis

APMA 3100 Probability

APMA 3110 Statistics and Applied Probability

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

 

Requirements

Foundational Programming Course

(Select one course)

DS 2001: Programming for Data Science (new)

CS 1110: Introduction to Programming

CS 1111: Introduction to Programming

CS 2110: Software Development Methods

Analytics Course

(Select one course)

DS 3001: Practice of Data Science (existing, currently 4001) (program description in Appendix IV)

STAT 3080: From Data to Knowledge

Systems Course

(Select one course)

DS 3002: Data Science Systems

CS 4750: Database Systems (for CS majors)

COMM 3220: Database Management Systems and Business (for undergrad commerce majors)

SYS 2202: Data and Information Engineering (Systems majors only)

Data Design or Value Course

(Select one course or suggest another course with Program Director approval)

DS 3003: Communicating with Data (new)

SARC 5400: Data Visualization

STATS 3280: Data Visualization and Management

COMM 3810 (3): Business Ethics

LPPP 4210 (3): Integrating Ethics in Public Policy

NASC 4200 (3): Leadership and Ethics

NUIP 3311 (3): Research, Ethics, Advocacy, and Leadership: Quality, Safety, and EBP

PHIL 3710 (3): Ethics

STS 4600 (3): The Engineer, Ethics, and Professional Responsibility

NUIP 4311 (3): Research, Ethics, Advocacy, and Leadership

 

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.

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