Master of Science in Data Science

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The Master of Science in Data Science (MSDS) is an 11-month professional master’s degree program. The residential program starts at the beginning of the third summer session and concludes at the end of the following Spring term. 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 integrates 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

(Summer Session III)

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
DS 6001 - Practice and Application of Data Science Credits: 2
DS 6011 - Data Science Capstone Project Work I Credits: 1
DS 6002 - Ethics of Big Data I Credits: 2
SYS 6018 - Data Mining Credits: 3

Spring Term

SYS 6016 - Machine Learning Credits: 3
DS 6003 - Practice and Application of Data Science II Credits: 2
DS 6013 - Data Science Capstone Project Work II Credits: 1

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 the program unless further approval is obtained.

CS 6160 - Theory of Computation Credits: 3
CS 6444 - Introduction to Parallel Computing Credits: 3
CS 6501 - Special Topics in Computer Science Credits: 3 (Topics approved by the DSI)
CS 6750 - Database Systems Credits: 3
ECON 7720 - Econometrics II Credits: 4
ECON 8720 - Time Series Econometrics Credits: 3
EVSC 7070 - Advanced Use of Geographical Information Systems Credits: 3
GCOM 7240 - Advanced Quantitative Analysis Credits: 4
PHS 5705 - Recent Advances in Public Health Genomics Credits: 3
PHS 7310 - Clinical Trials Methodology Credits: 3
PSYC 5720 - Fundamentals of Item Response Theory Credits: 3
PSYC 7760 - Introduction to Applied Multivariate Methods Credits: 3
SARC 5400 - Data Visualization Credits: 3
STAT 6250 - Longitudinal Data Analysis Credits: 3
STAT 6260 - Categorical Data Analysis Credits: 3
SYS 6023 - Cognitive Systems Engineering Credits: 3
SYS 6050 - Risk Analysis Credits: 3
SYS 6582 - Selected Topics in Systems Engineering Credits: 3 (Topics approved by the DSI)
SYS 7001 - System and Decision Sciences Credits: 3

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