The B.S. in Computational Modeling and Data Analytics (CMDA) is Virginia Tech's big data degree. Would you like to learn how to use fast algorithms to model the world and discover hidden patterns in massive data sets? Do you want to write code that runs at blazing speed on the fastest computers? CMDA students can combine mathematics, statistics, and computer science to solve important practical problems in applications like social network analysis, homeland security, disease spread, cancer therapy, and tsunami prediction.
CMDA majors learn practical skills that are valued by industry:
For what kind of jobs will CMDA majors qualify?
The possibilities include:
CMDA students learn to be dynamic problem solvers who can work collaborate in interdisciplinary teams. Modern computational science draws on a variety of expertise, and our students practice these skills particularly in the CMDA Capstone Project course. Teams of CMDA majors spend the semester tackling an interesting data/modeling problem presented from a client (elsewhere on campus, or in industry).
The CMDA requirements give students the flexibility to dive deeply into a particular area through choice of electives, a disciplinary track, or a minor or double major. Possibilities include a CMDA-Physics track or minors in fields as diverse as entrepreneurship or actuarial science.
The 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, CMDA teaches a rich suite of quantitative skills for tackling today's massive data-based problems. CMDA courses focus on extracting information from large data sets, and on analyzing and solving problems through fast algorithms, accurate models, evolving statistical methodology, and quantifying uncertainty. Drawing on massive computational resources, these skills enable powerful analytic techniques impossible just a few years ago. Graduates are qualified for positions in industry, business, the sciences, engineering, and more – anywhere top-flight quantitative scientists are needed.
Any students who would like to seek approval for a course to be added to our Restricted Electives list can submit a request. Students are advised to seek approval for these substitute electives before taking them; students transferring into CMDA should seek approval for any previous courses during their first semester in the major. Under no circumstances should approval be sought during the semester before graduation. Please make your request using this form.
What Companies are Saying About CMDA:
"The (CMDA) program directly aligns with the needs of our business as we have witnessed the need grow regularly for employees that combine proficiency in both quantitative skills as well as skills in the physical and quantitative sciences."
"The concept and strengths of the (CMDA) curriculum are unique and are essential to advancing capabilities of our industry and of any industry relying on information."
"We believe that this collaborative initiative, combining cutting edge curriculum and research from multiple scientific and engineering disciplines with the strengths of industry mentors can lead to advances in data intelligence that address critical state and national needs. IBM has precise interests in this new major. Specifically, we are interested in participating in the development of a workforce suitable for creative use of massive datasets for predictive, actionable, and risk analysis."
"...we need a workforce with the types of skills that the Virginia Tech CMDA graduates will have developed and honed during their undergraduate years."
"Extreme Networks has specific interests in the CMDA degree program. As a computer networking equipment manufacturer with application analytics software for wired and wireless networks … we are especially interested in the development of a workforce suitable for creative use of massive datasets. These skills are highly valuable for making sense of all of the vast data flowing across networks and creating new tools to mine this wealth of data – for social media, real-time communications, web searching, cloud storage, and many other applications."
CMDA is directed by Professor Mark Embree of the Math Department and Nora Sullivan is the program manager and academic advisor. Please contact Dr. Embree at firstname.lastname@example.org or Nora Sullivan at email@example.com for more information on the CMDA program.
Program Manager and Advisor for the
Academy of Integrated Science
College of Science
Academic Program Specialist
Academy of Integrated Science
College of Science