Summary
Virginia Tech’s Academy of Data Science was launched in 2020, in part to help meet the growing demand for workers who possess the skills to analyze data. Data science — a transdisciplinary field that draws upon the theories, methods, and concepts of statistics, mathematics, computer science, and information science to extract knowledge and insight from data — not only impacts all branches of science, but other fields as well.
The Academy of Data Science will focus on the development of methods, techniques, and tools for extracting knowledge and insight from data to further science. In doing so, it will elevate data science as a scientific discipline of its own, as well as bolstering the integration of data science into all scientific fields. Additionally, the Academy will serve as the connective fabric between the College of Science and other Virginia Tech colleges and institutes as they collaborate to develop new data science methodologies and applications of data science in scientific disciplines.
Tom Woteki, a three-time Virginia Tech alum with a Ph.D. in statistics, was named the founding director of the Academy of Data Science. He also heads the part-time data analysis and applied statistics master’s degree program in the greater Washington, D.C., metro area.
Source: VT Website
Virginia Tech College of Science – 14/06/2024 (00:25)
As a degree requirement, seniors in the Virginia Tech Computational Modeling & Data Analytics (CMDA) program — part of the Academy of Data Science — must enroll in the Capstone Course.
Each semester, teams of 3-4 students collaborate to work with a government, industry, or institutional partner to solve a real-world problem using data science.
This video introduces Skinny Genes, a team that worked with the Virginia Tech Center for Biostatistics and Health Data Science for its Capstone project.
OnAir Post: Virginia Tech Academy of Data Science
News
Name: Yihan Liu
Majors: Computational modeling and data analytics & mathematics
Hometown: China
Plans after graduation: Go to graduate school to pursue a Ph.D. in robotics (ECE) at Georgia Tech
Favorite professor/class: Dr. Lizette Zietsman/MATH 2405 & 2406
Yihan Liu — a native of China who spent his high school years in Northern Virginia — knew early on that attending Virginia Tech for his undergraduate degree with a full scholarship would be the right choice.
Yili Hong, professor of statistics in the College of Science at Virginia Tech, has been appointed to the Data Science Faculty Fellowship.
The Data Science Faculty Fellowships were established by the College of Science in 2021 to enhance the national and international prominence of Virginia Tech faculty in this field. The fellowships provide support for outstanding scholarship in data science or the application of data science techniques within and across disciplines.
Hong joined the Department of Statistics in 2009 as an assistant professor. He currently serves as professor and co-director of the VT Statistics and Artificial Intelligence Laboratory. Hong’s role in shaping the future of data science is demonstrated in multiple ways with research designed to address three fundamental questions: how to collect data, how to interpret data, and how to use data to predict the future, which are the core tasks at the heart of data science.
A new ChatGPT model invalidates unsupervised online testing for academia and pre-employment, according to research from a Virginia Tech psychologist.
Increasingly beleaguered by artificial intelligence (AI)-based cheating, unproctored testing is now completely vulnerable to reasoning large language models (LLM) like ChatGPT. A Virginia Tech psychologist has a few ideas about where to go from here.
Unproctored, or unsupervised, online tests allow employers and instructors to evaluate thousands of people simultaneously and score them in seconds, delivering quick results.
OpenAI’s new o1 model — the earliest of the recent wave of reasoning LLMs — renders unproctored assessments worthless, according to recent research from a Virginia Tech psychologist published in the International Journal of Selection and Assessment.
“The findings suggest that few, if any, unproctored tests will remain safe from people using large language models to cheat,” said Louis Hickman, an industrial organizational psychologist who studies the implications of artifcial intelligence in workplaces.
About
Meet Our Team
Tom Woteki
Director, Academy of Data Science
Program Director, M.S. in Data Science
Professor of Practice, Statistics
Email
Lizette Zietsman
Associate Director, Academy of Data Science
Associate Director, Computational Modeling & Data Analytics
Associate Professor, Mathematics
Email
Mark Embree
Program Director, Computational Modeling & Data Analytics
Professor, Mathematics
Email
Cherie Nelson
Executive Assistant to the Director, Academy of Data Science
Program Coordinator, M.S. in Data Science
Email
Melissa McKeown
Communications Manager, Academy of Data Science
Email
Source: VT Website
Computational Modeling & Data Analytics
The Computational Modeling and Data Analytics (CMDA) program draws on expertise from three primary departments at Virginia Tech with strengths in quantitative science: Mathematics, Statistics, and Computer Science. By combining elements of these disciplines in innovative, integrated courses that emphasize techniques at the forefront of applied computation, we teach a rich suite of quantitative skills for tackling today’s massive data-based problems.
Career Opportunities
- Environment / energy / sustainability
- Management consulting / government contracting
- Healthcare
- Banking / financial services / insurance
- Medical / biomedical / pharmaceutical
- Sports analytics
- National security / defense / space / crypto
- Design and manufacturing
- Big tech / cloud computing
- Media and marketing
- Public policy
Source: VT Website
M.S. Data Science
Virginia Tech’s Master of Science in Data Science stands out by bridging the gap between theoretical knowledge and successful practical application. The degree combines rigorous technical training with real-world projects, preparing graduates to navigate organizational complexities and translate data into action and value. The faculty’s extensive industry experience delivers a blend of technical expertise and industry understanding to give graduates a distinct edge.
Highlights:
- Prepares students for careers in one of the world’s fastest growing job fields
- Combines the perfect blend of practice and theory
- Taught by industry experts with decades of experience in the field of data science
- Features electives in economics, forest resources and environmental conservation, geosciences, and industrial and systems engineering, alongside the more traditional subjects of computer science, mathematics, and statistics
- Enables students to gain hands-on, real-world experience through a Capstone project that is completed in the second year of study
- Initially offered as 4+1 accelerated degree in Fall 2025 (standard two-year option will launch in Fall 2026)
Source: VT Website
Data & Decisions (Minor)
At the completion of the Data and Decisions (DTDC) minor, students will be able to consume and use data responsibly, ethically, collaboratively, effectively, and efficiently. Such skills are both marketable professionally and useful within personal lives because data literacy is essential for living productive and healthy lives in the 21st century.
Requirements
The 18-credit hour minor in Data and Decisions includes 3 hours of introductory restricted electives, 6 hours of core requirements, 6 hours of elective courses selected from a list, and 3 hours of a Data and Decisions Capstone.
Required courses:
CMDA 2014: Data Matter
BDS 2005: Fundamentals of Behavioral Decision Science
BIT/MGT 4854: Analytics in Action (capstone)
Elective courses:
Students select 3 hours of introductory restricted electives, and another 6 credit hours of elective courses from topics such as environmental informatics; introduction to remote sensing; social research methods; digital history; data governance; privacy and ethics. For a complete list of elective courses, consult the checksheet.
Source: VT Website
Contact
Email: School
Web Links
Videos
Skinny Genes – Spring 2024 CMDA Capstone Course
June 14, 2024 (00:25)
By: Virginia Tech College of Science
As a degree requirement, seniors in the Virginia Tech Computational Modeling & Data Analytics (CMDA) program — part of the Academy of Data Science — must enroll in the Capstone Course.
Each semester, teams of 3-4 students collaborate to work with a government, industry, or institutional partner to solve a real-world problem using data science.
This video introduces Skinny Genes, a team that worked with the Virginia Tech Center for Biostatistics and Health Data Science for its Capstone project.
