The Minor in Data Science will expose students to the conceptual framework for the emerging field of data science, including four central areas of focus: analytics, systems (data engineering), design (communications), and value (ethics). These components combine to form the practice of data science. Through exposure to these critical domains of data science, students in the minor will receive the education needed to understand rapidly evolving data science concepts from theory to practice.
Earning a minor in data science will prepare you for careers or post-graduate work using data skills, providing a solid foundation for further exploration in the field. Join our community of undergraduate data science students!
Minor Requirements listed based on the Plan Requirement Term Shown in the Student's SIS Account:
Plan Requirements for Terms Fall 2021, January 2022, Spring 2022, & Summer 2022
Minor students are required to take one course from each of the five categories listed below, totaling 15 credits; two courses (6 credits) must be data science (DS) courses.
Foundational Programming Course (select one)
- DS 1002 (formerly 2001): Programming for Data Science
- CS 1110: Introduction to Programming
- CS 1111: Introduction to Programming
- CS 1112: Introduction to Programming
- CS 1113: Introduction to Programming
- CS 2110: Software Development Methods
- PHYS 1655: Introduction to Python for Scientists and Engineers
Analytics Course (select one)
- DS 2006: Computational Probability
- DS 3001 (formerly 4001 Practice of Data Science): Foundations of Machine Learning
- DS 3005: Mathematics for Data Science
- DS 3006: Inference and Prediction
- CS 4774: Machine Learning
- PSYC 5710: Machine Learning and Data Mining
- STAT 5630: Statistical Machine Learning
- APMA 3150: From Data to Knowledge
- STAT 3080: From Data to Knowledge
Systems Course (select one)
- DS 2002 (formerly 3002): Data Science Systems
- CS 4750: Database Systems (for CS majors)
- COMM 3220: Database Management Systems and Business (for Commerce majors)
- SYS 2202: Data and Information Engineering (for Systems Engineering majors)
Data Design or Value Course (select one)
Recommended Courses:
- DS 2003 (formerly 3003): Communicating with Data
- DS 2004: Data Ethics
- DS 4003 Data Design II: Interactive Applications
- SARC 5400: Data Visualization
- APMA 2500 / STS 2500: Ethical Analytics
The below courses are also options:
- COMM 3810: Business Ethics
- COMM 4520: Data Exploration and Visualization with R
- LPPP 4210: Integrating Ethics in Public Policy
- NASC 4200: Leadership and Ethics
- NUIP 3311: Research, Ethics, Advocacy, and Leadership: Quality, Safety, and EBP
- PLAN 3813-001: Methods of Community Research and Engagement, School of Architecture
- LPPS 5360: Imagining Equitable Policy
- STS 4600: The Engineer, Ethics, and Professional Responsibility
- NUIP 4311: Research, Ethics, Advocacy, and Leadership
Domain Elective or Final Project (select one)
DS 4002: Data Science Project Course - Students work in groups to complete several projects throughout the semester using different types of data (tabular, images, text, time, etc.). AFTER JANUARY-TERM 2025, THIS COURSE WILL NO LONGER BE OFFERED DURING FUTURE J-TERMS. IT WILL ONLY BE OFFERED DURING FALL AND SPRING TERMS.
Students can also select one of the following data science applied electives:
- BME 3636: Neural Network Models
- CHE 4561: Data Science in Chemical Engineering
- COMM 4351 - Marketing Analytics for Big Data
- COMM 4230: Info Technology in Finance
- COMM 4520: Ethical Application of Artificial Intelligence
- COMM 4522: Business Analytics with Python
- CS 3710: Introduction to Cybersecurity
- CS 4501 Human-Robot Interaction
- CS 4774: Machine Learning
- DS 5110 Big Data Systems
- ENGL 3500: Hacking for Humanists
- EVSC 3020 GIS Methods
- EVSC 4080: Quantitative Methods in Environmental Sciences
- EVSC 5040 Messy Data
- GSVS 4100 Evidence for Sustainability Policy
- PHS 3102: Introduction to Public Health Research: Population Data Analysis
- STAT 4220 Applied Analytics for Business
- STAT 4630: Statistical Machine Learning
- STAT 4800 Advanced Sports Analytics I (formerly STAT 4559 Sports Analytics)
- STAT 4996: Capstone
Plan Requirements for Terms Fall 2022, January Term 2023, & Spring 2023
Minor students are required to take one course from each of the five categories listed below, totaling 15 credits; three courses (9 credits) must be data science (DS) courses.
Foundational Programming Course (select one)
- DS 1002: Programming for Data Science
- CS 1110: Introduction to Programming
- CS 1111: Introduction to Programming
- CS 1112: Introduction to Programming
- CS 1113: Introduction to Programming
- CS 2110: Software Development Methods
- PHYS 1655: Introduction to Python for Scientists and Engineers
Analytics Course (select one)
- DS 2006: Computational Probability
- DS 3001: Foundations of Machine Learning
- DS 3005: Mathematics for Data Science
- DS 3006: Inference and Prediction
- CS 4774: Machine Learning
- PSYC 5710: Machine Learning and Data Mining
- STAT 5630: Statistical Machine Learning
- APMA 3150: From Data to Knowledge
- STAT 3080: From Data to Knowledge
Systems Course (select one)
- DS 2002 (formerly 3002): Data Science Systems
- CS 4750: Database Systems (for CS majors)
- COMM 3220: Database Management Systems and Business (for Commerce majors)
- SYS 2202: Data and Information Engineering (for Systems Engineering majors)
Data Design or Value Course (select one)
Recommended Courses:
- DS 2003 (formerly 3003): Communicating with Data
- DS 2004: Data Ethics
- DS 4003 Data Design II: Interactive Applications
- SARC 5400: Data Visualization
- APMA 2500 / STS 2500: Ethical Analytics
The below courses are also options:
- COMM 3810: Business Ethics
- COMM 4520: Data Exploration and Visualization with R
- LPPP 4210: Integrating Ethics in Public Policy
- NASC 4200: Leadership and Ethics
- NUIP 3311: Research, Ethics, Advocacy, and Leadership: Quality, Safety, and EBP
- PLAN 3813-001: Methods of Community Research and Engagement, School of Architecture
- LPPS 5360: Imagining Equitable Policy
- STS 4600: The Engineer, Ethics, and Professional Responsibility
- NUIP 4311: Research, Ethics, Advocacy, and Leadership
Domain Elective or Final Project (select one)
DS 4002: Data Science Project Course - Students work in groups to complete several projects throughout the semester using different types of data (tabular, images, text, time, etc.). AFTER JANUARY-TERM 2025, THIS COURSE WILL NO LONGER BE OFFERED DURING FUTURE J-TERMS. IT WILL ONLY BE OFFERED DURING FALL AND SPRING TERMS.
Students can also select one of the following data science applied electives:
- BME 3636: Neural Network Models
- CHE 4561: Data Science in Chemical Engineering
- COMM 4351 - Marketing Analytics for Big Data
- COMM 4230: Info Technology in Finance
- COMM 4520: Ethical Application of Artificial Intelligence
- COMM 4522: Business Analytics with Python
- CS 3710: Introduction to Cybersecurity
- CS 4501 Human-Robot Interaction
- CS 4774: Machine Learning
- DS 5110 Big Data Systems
- ENGL 3500: Hacking for Humanists
- EVSC 3020 GIS Methods
- EVSC 4080: Quantitative Methods in Environmental Sciences
- EVSC 5040 Messy Data
- GSVS 4100 Evidence for Sustainability Policy
- PHS 3102: Introduction to Public Health Research: Population Data Analysis
- STAT 4220 Applied Analytics for Business
- STAT 4630: Statistical Machine Learning
- STAT 4800 Advanced Sports Analytics I (formerly STAT 4559 Sports Analytics)
- STAT 4996: Capstone
Plan Requirements for Terms Summer 2023, Fall 2023, January 2024, Spring 2024, & Summer 2024
Minor students are required to take one course from each of the five categories listed below, totaling 15 credits; three courses (9 credits) must be data science (DS) courses.
Foundational Programming Course (select one)
- DS 1002: Programming for Data Science
- CS 1110: Introduction to Programming
- CS 1111: Introduction to Programming
- CS 1112: Introduction to Programming
- CS 1113: Introduction to Programming
- CS 2110: Software Development Methods
- PHYS 1655: Introduction to Python for Scientists and Engineers
Analytics Course (select one)
- DS 2006: Computational Probability
- DS 3001: Foundations of Machine Learning
- DS 3005: Mathematics for Data Science
- DS 3006: Inference and Prediction
- CS 4774: Machine Learning
- PSYC 5710: Machine Learning and Data Mining
- STAT 5630: Statistical Machine Learning
Systems Course (select one)
- DS 2002: Data Science Systems
- CS 4750: Database Systems (for CS majors)
- COMM 3220: Database Management Systems and Business (for Commerce majors)
- SYS 2202: Data and Information Engineering (for Systems Engineering majors)
Data Design or Value Course (select one)
Recommended Courses:
- DS 2003: Communicating with Data
- DS 2004: Data Ethics
- DS 4003 Data Design II: Interactive Applications
- SARC 5400: Data Visualization
- APMA 2500 / STS 2500: Ethical Analytics
The below courses are also options:
- COMM 3810: Business Ethics
- COMM 4520: Data Exploration and Visualization with R
- LPPP 4210: Integrating Ethics in Public Policy
- NASC 4200: Leadership and Ethics
- NUIP 3311: Research, Ethics, Advocacy, and Leadership: Quality, Safety, and EBP
- PLAN 3813-001: Methods of Community Research and Engagement, School of Architecture
- LPPS 5360: Imagining Equitable Policy
- STS 4600: The Engineer, Ethics, and Professional Responsibility
- NUIP 4311: Research, Ethics, Advocacy, and Leadership
Domain Elective or Final Project (select one)
DS 4002: Data Science Project Course - Students work in groups to complete several projects throughout the semester using different types of data (tabular, images, text, time, etc.). AFTER JANUARY-TERM 2025, THIS COURSE WILL NO LONGER BE OFFERED DURING FUTURE J-TERMS. IT WILL ONLY BE OFFERED DURING FALL AND SPRING TERMS.
Students can also select one of the following data science applied electives:
- BME 3636: Neural Network Models
- CHE 4561: Data Science in Chemical Engineering
- COMM 4351 - Marketing Analytics for Big Data
- COMM 4230: Info Technology in Finance
- COMM 4520: Ethical Application of Artificial Intelligence
- COMM 4522: Business Analytics with Python
- CS 3710: Introduction to Cybersecurity
- CS 4501 Human-Robot Interaction
- DS 5110 Big Data Systems
- ENGL 3500: Hacking for Humanists
- EVSC 3020 GIS Methods
- EVSC 4080: Quantitative Methods in Environmental Sciences
- EVSC 5040 Messy Data
- GSVS 4100 Evidence for Sustainability Policy
- PHS 3102: Introduction to Public Health Research: Population Data Analysis
- STAT 4220 Applied Analytics for Business
- STAT 4630: Statistical Machine Learning
- STAT 4800 Advanced Sports Analytics I (formerly STAT 4559 Sports Analytics)
- STAT 4996: Capstone
Plan Requirements for Terms Fall 2024 and later
Minor students are required to take one course from each of the five categories listed below, totaling 15 credits; three courses (9 credits) must be data science (DS) courses.
Foundational Programming Course (select one)
- DS 1002: Programming for Data Science
- CS 1110: Introduction to Programming
- CS 1111: Introduction to Programming
- CS 1112: Introduction to Programming
- CS 1113: Introduction to Programming
- CS 2110: Software Development Methods
- PHYS 1655: Introduction to Python for Scientists and Engineers
The Foundational Programming requirement will not be waived or substituted with another course, if you have questions or have taken the C1110 place-out test, email SDSMinor@virginia.edu to understand the course you need to enroll in to fulfill this requirement.
Analytics Course (select one)
- DS 3001: Foundations of Machine Learning
- CS 4774: Machine Learning
- PSYC 5710: Machine Learning and Data Mining
- STAT 5630: Statistical Machine Learning
Systems Course (select one)
- DS 2002: Data Science Systems
- CS 4750: Database Systems (for CS majors)
- COMM 3220: Database Management Systems and Business (for Commerce majors)
- SYS 2202: Data and Information Engineering (for Systems Engineering majors
Data Design or Value Course (select one)
Recommended Courses:
- DS 2003: Communicating with Data
- DS 2004: Data Ethics
- SARC 5400: Data Visualization
- APMA 2500: Ethical Analytics
- STS 2500: Ethical Analytics
The below courses are also options:
- COMM 3810: Business Ethics
- COMM 4520: Data Exploration and Visualization with R
- LPPP 4210: Integrating Ethics in Public Policy
- NASC 4200: Leadership and Ethics
- NUIP 3311: Research, Ethics, Advocacy, and Leadership: Quality, Safety, and EBP
- PLAN 3813-001: Methods of Community Research and Engagement, School of Architecture
- LPPS 5360: Imagining Equitable Policy
- STS 4600: The Engineer, Ethics, and Professional Responsibility
- NUIP 4311: Research, Ethics, Advocacy, and Leadership
Domain Elective or Final Project (select one)
DS 4002: Data Science Project Course - Students work in groups to complete several projects throughout the semester using different types of data (tabular, images, text, time, etc.). AFTER JANUARY-TERM 2025, THIS COURSE WILL NO LONGER BE OFFERED DURING FUTURE J-TERMS. IT WILL ONLY BE OFFERED DURING FALL AND SPRING TERMS.
Students can also select one of the following data science applied electives:
- BME 3636: Neural Network Models
- CHE 4561: Data Science in Chemical Engineering
- COMM 4351 - Marketing Analytics for Big Data
- COMM 4230: Info Technology in Finance
- COMM 4520: Ethical Application of Artificial Intelligence
- COMM 4522: Business Analytics with Python
- CS 3710: Introduction to Cybersecurity
- CS 4501 Human-Robot Interaction
- DS 5110 Big Data Systems
- ENGL 3500: Hacking for Humanists
- EVSC 3020 GIS Methods
- EVSC 4080: Quantitative Methods in Environmental Sciences
- EVSC 5040 Messy Data
- GSVS 4100 Evidence for Sustainability Policy
- PHS 3102: Introduction to Public Health Research: Population Data Analysis
- STAT 4220 Applied Analytics for Business
- STAT 4630: Statistical Machine Learning
- STAT 4800 Advanced Sports Analytics I (formerly STAT 4559 Sports Analytics)
- STAT 4996: Capstone
