Curriculum

The Master of Science in Data Science (MSDS) is an 11-month professional masters program. The program starts in mid-July and ends the following year in mid-May. Core program courses are taught by faculty from the departments of Computer Science, Statistics, and Systems and Information Engineering. The program utilizes the spiral learning framework, first giving the students the base that they need in languages, computation, and linear modeling and building upon those skills to move to the practice and application of data science. The program culminates in a capstone project where the students use their knowledge base to solve a real world data science problem and present the results in a public presentation.

 

Course requirements for the MSDS program:

Summer Term (approximately 4 weeks, starting mid-July):

  • CS 5010: Programming and Systems for Data Science  Credits: 3
  • STAT 6430: Statistical Computing for Data Science Credits:3

Fall Term:

  • STAT 6021: Linear Models for Data Science Credits: 3
  • CS 5012: Foundations of Computer Science Credits: 3
  • SYS 6018: Data Mining Credits: 3
  • DS 6001: Practice and Application of Data Science Credits:2
  • DS 6002: Ethics of Big Data Credits: 1
  • DS 6011: Capstone Project Credits:1

Spring Term:

  • SYS 6016: Machine Learning Credits: 3
  • DS 6012: Ethics of Big Data II Credits: 1
  • DS 6003: Practice and Application of Data Science II Credits: 1
  • DS 6013: Data Science Capstone Project Work II Credits: 2
  • Elective 1
  • Elective 2

Elective:

Selection of elective courses is done in consultation with the program director. There are a variety of possible electives available, including (but not limited) to those suggested in the Graduate Record. Students are required to take 6 credits of elective credit.  In the established curriculum, these 6 credits fall within the spring term.  Elective courses must be at the 5000-level or higher to count for elective credit in program unless further approval is obtained.

 

  • CS 6501: Special Topics in Computer Science Credits: 3
  • CS 5014: Computation as a Research Tool Credits: 3
  • CS 6750: Database Systems Credits:3
  • APMA 7548: Selected Topics in Applied Mathematics Credits: 3
  • STAT 6250:Longitudinal Data analysis Credits:3 
  • MATH 5330: Advanced Multivariate Calculus Credits:3
  • MATH 7310: Real Analysis and Linear Spaces I Credits:3
  • ELSC 7070: Advanced Use of Geographical Information Systems Credits 3
  • GCOM 7280: Big Data Credits: 1.5
  • GCOM 7260: Cloud Computing Credits: 1.5

 

For course details and descriptions please see the UVA Course Catalog.

Other electives are possible, depending on available courses.

“The information contained on this website is for informational purposes only.  The Undergraduate Record and Graduate Record represent the official repository for academic program requirements. These publications may be found at http://records.ureg.virginia.edu/index.php.”