Master of Science in Data Science

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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 6 weeks, starting mid-July)

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
DS 6001 - Practice and Application of Data Science Credits: 2

January Term

DS 6002 - Ethics of Big Data I Credits: 1

Spring Term

SYS 6018 - Data Mining Credits: 3
SYS 6016 - Machine Learning Credits: 3
DS 6003 - Data Science Capstone Project Credits: 3

Elective

Selection of elective courses is done in consultation with the program director. There are a variety of possible electives available, including the following:

Fall Term:

CS 6501 - Special Topics in Computer Science Credits: 3
CE 6400 - Traffic Operations Credits: 3
SYS 6001 - Introduction to Systems Engineering Credits: 3
SYS 6003 - Optimization I Credits: 3
SYS 6005 - Stochastic Systems I Credits: 3

Spring Term:

CS 6750 - Database Systems Credits: 3
STAT 5170 - Applied Time Series Credits: 3
STAT 5340 - Bootstrap and Other Resampling Methods Credits: 3
MATH 4110 Introduction to Stochastic Processes - Credits 3

For course details and descriptions please see the UVA Course Catalog.
Other electives are possible, depending on available courses.