What courses are in an MS in Data Science?

Courses offered in the MS in Data Science program range from Informatics to Biostatistics, to Healthcare Analysis.

MS in Data Science Core and Elective Courses

MS in Data Science students will earn their degree with 18 credits of required core courses, 9 credits of electives, and a 3-credit applied capstone project.

There are multiple concentrations offered for students within the MS in Data Science program. Each concentration will specify 9 credits of prescribed coursework. Students can choose to graduate with or without a concentration.  Students without a concentration can take 9 credits of their choosing following consultation with their academic advisor.

Core courses are designed and taught with the MS in Data Science student in mind. Students who are not in the MS in Data Science may be permitted to enroll in core courses on a space-available basis with the permission of the Faculty of Record and MS Academic Program Director. Elective courses are designed and taught with the MS in Data Science student in mind or with multiple graduate student populations in mind.

While most full-time students will complete the program in 11 months (fall, spring, summer), some full-time students may decide to devote a longer time for program completion, with a minimum of 9 credits needed fall/spring to maintain full-time status.

Application Deadline

Fall 2022: July 15 at 11:59 PM EDT.

International applicants are encouraged to apply on or before June 1

Sample General Plan of Study for Full Time Program

Fall Spring Summer
STAT 5405 (3 credits)
Applied Statistics for Data Science
STAT 5125
Computing for Statistical Data Science (3 credits)
GRAD 5800
Capstone Applied Project[i] (3 credits)
CSE 5713
Data Mining and Management (3 credits)
OPIM 5501[ii]
Data Visualization and Communication (2 credits)
Elective 1 (3 credits) CSE 5819
Introduction to Machine Learning (3 credits)
Research Design and Measurement
for Data Science
(2 credits)
Elective 2 (3 credits)
ARE 5353
Data Ethics and Equity (2credits)
Elective 3 (3 credits
13 credits  14 credits  3 credits

[i] Applied Capstone could be done spring or summer. Capstone could be team-based, project-based, with a company sponsor. Capstone could also be arranged on an individual basis with a faculty advisor. Other approaches should be considered as well. Procuring industry sponsor---sponsorship fee when possible (Data Science Administrative Director). Organizing students into teams (Faculty). Need to be sensitive to DHS restrictions and access to restricted projects for non-U.S. citizens.

[ii] OPIM5501 is an existing 3 credit hour course that will need to be reworked/renumbered through the C&C process to reflect 2 credits and acknowledge the additional focus on communication as indicated by the course title Data Visualization and Communication.

Required Courses

Required Core Courses:
STAT 5405 X
STAT 5215 X
CSE 5819 X
CSE 5713  
OPIM 5501  
EPSY 5641  
ARE 5353  
Required Capstone Project
GRAD 5800 X
STAT 5915
Elective Courses by Concentration
BIST 5625 X
BIST 5645 X
BIST 5615 X every two years
Dependent Data Analysis
BIST 5825 X
BIST 5815 X
BIST 5845 X every two years
Advanced Data Analysis
STAT 5415 X
STAT 5665 X
STAT 5675 X every two years
CSE 5800 X
CSE 5815 X
CSE 5840 X
CSE 5860 X
CSE 5850 X
CSE 5852   X
CSE 5854   X
Cloud Computing
CSE 5299 X
CSE 5300   X
CSE 5304   X
CSE 5309   X
Social and Behavioral Analysis
EPSY 6615   X
EPSY 6611 X
Geospatial Data Analysis
Required Courses:  
NRE 5525 X
NRE 5585 X
NRE 5215 X
NRE 5545 X
NRE 5560   X
NRE 5235   X
Business Data Science
OPIM 5501 X X X
OPIM 5502 X X X
OPIM 5504 X
OPIM 5509 X X
OPIM 5511   X
OPIM 5512 X X X
Marketing Analytics
Required Courses:  
MKTG 5115 X X X
MKTG 5220   X Every other Summer
MKTG 5250   X Every other Summer
MKTG 5251   X
MKTG 5665 X X
OPIM 5510 Every other spring and fall
Talent Analytics
MGMT 5680 X
MGMT 5377   X
MGMT 5650 X X X
MGMT 5674 X X X
MGMT 5675 X
Healthcare Analytics
HCMI 5240 X
HCMI 5243   X
HCMI 5686   X
OPIM 5508 Alternates every other spring and fall

Data Science Course Descriptions


BIST 5625: Introduction to Biostatistics
Rates and proportions, sensitivity, specificity, two-way tables, odds ratios, relative risk, ordered and non-ordered classifications, rends, case-control studies, elements of regression including logistic and Poisson, additivity and interaction, combination of studies and meta-analysis.
Prerequisites: Open to graduate students in Biostatistics, others with permission.

BIST 5645: Concepts and Analysis of Survival Data
Survival models, censoring and truncation, nonparametric estimation of survival functions, comparison of treatment groups, mathematical and graphical methods for assessing goodness of fit, parametric and nonparametric regression models.
Prerequisites: Open to graduate students in Biostatistics, others with permission.

BIST 5615: Categorical Data Analysis
Statistical analysis of data on a nominal scale: discrete distributions, contingency tables, odds ratios, interval estimates, goodness of fit tests, logistic/probit/complementary log-log regression, Poisson-related regression.
Prerequisites: STAT 5405 or BIST 5505 and 5605, or instructor consent.

Dependent Data Analysis

BIST 5815: Longitudinal Data Analysis
Statistical theory and methodology for data collected over time in a clustered manner: design of experiments, exploratory data analysis, linear models for continuous data, general linear models for discrete data, marginal and mixed models, treatment of missing data.
Prerequisites: STAT 5405 or BIST 5505 and BIST 5605, or instructor consent.

STAT 5825: Applied Time Series
Introduction to prediction using time-series regression methods with non-seasonal and seasonal data. Smoothing methods for forecasting. Modeling and forecasting using univariate autoregressive moving average models.
Prerequisites: Open to graduate students in Statistics, others with permission.

STAT 5845: Applied Spatio-Temporal Statistics
Applied statistical methodology and computing for spatio-temporal data, including visualization, models, and inferences. Extreme value analysis in spatio-temporal contexts will be a module. Focus will be on models that account for spatio-temporal dependence and inferences that provide appropriate uncertainty measures, with applications to real-world problems using open-source software.
Prerequisites: Open to graduate students in Statistics; others with per-mission.
Recommended preparation: STAT 5405 or 5605 or GEOG 5600 or 5610 or ERTH 5150 or equivalent.

Advanced Data Analysis

STAT 5415: Statistical Methods for Data Science
Basic probabilistic concepts; marginal, joint and conditional probability distributions; point and interval estimation; and hypothesis testing.
Prerequisites: Differential calculus; introductory course in statistics; and Instructor consent. Not open to students who have passed STAT 5585 or STAT 5685 or BIST 5585 or BIST 5685.

STAT 5665: Applied Multivariate Analysis
Multivariate normal distributions, inference about a mean vector, comparison of several multivariate means, principal components, factor analysis, canonical correlation analysis, discrimination and classification, cluster analysis.
Prerequisites: Open to graduate students in Statistics, others with permission.

STAT 5675: Bayesian Data Analysis
Theory of statistical inference based on Bayes’ Theorem: basic probability theory, linear/nonlinear, graphical, and hierarchical models, decision theory, Bayes estimation and hypothesis testing, prior elicitation, Gibbs sampling, the Metropolis-Hastings algorithm, Monte Carlo integration.
Prerequisites: STAT 5585 and STAT 5685, or instructor consent.

STAT 5915: Statistical Data Science in Action
Real-world statistical data science practice: problem formulation; integration of statistics, computing, and domain knowledge; collaboration; communication; reproducibility; project management.
Prerequisites: STAT 5405 or instructor consent.


UConn’s Master’s in Data Science is an 11-month cohort based full-time in-person program and is eligible for F-1 and J-1 visa sponsorship. Courses are offered on the University of Connecticut’s Storrs, CT USA Campus. This program is eligible for the STEM OPT extension that affords certain F-1 graduate students an opportunity to apply for a 24-month extension of their post-completion optional practical training (OPT). The program does not offer graduate assistantships and scholarships at this time.