Earn your MS in Data Science

The Master of Science program in data science requires 30s.h. of graduate credit. Students must maintain a grade-point average of at least 3.00 in all work toward the degree and in additional relevant coursework. The program can be completed in three or four semesters.

Coursework includes core courses covering the fundamentals of data science including probability and statistics; data storage, access, and management; and data visualization, exploration, modeling, analysis, and uncertainty quantification. Students acquire hands-on experience in solving real-world problems, communication skills, and data ethics via a required capstone project. Students may choose electives from a wide variety of courses on specialized data science topics offered by the departments of Statistics and Actuarial Science, Computer Science, Business Analytics, and Biostatistics to enhance their skill sets based on their interests and career goals.

Requirements and program planning

Students who apply to our MS program should have two semesters of calculus or two semesters of engineering calculus.


The MS with a major in data science requires the following coursework.

All of These
Course numberCourse nameSemester offeredSemester hours
DATA:3120/STAT:3120  Probability and StatisticsFall4
DATA:3200/STAT:3200Applied Linear Regression Spring and fall3
DATA:4540/STAT:4540Statistical LearningFall3
DATA:4580/STAT:4580Data Visualization and Data Technologies Spring3
DATA:4600Casual Inferences for Data ScienceSpring3
DATA:4750/STAT:4750Probabilistic Statistical LearningSpring3
DATA:5400/STAT:5400Computing in StatisticsFall3
Choose one of these: DATA:5890 (2 s.h.) or DATA:4890 (3 s.h.)MS Data Science Practicum Spring and fall2-3
DATA:6220Consulting and Communication with DataSpring3
DATA:7400Computer Intensive StatisticsSpring 3


The following course may be substituted if the required courses have been taken, with advisor's approval.
Course numberCourse nameSemester offeredSemester hours
ACTS:6200/DATA:6200Predictive AnalyticsSpring3
BAIS:6100Text AnalyticsTBD3
BAIS:6130Applied OptimizationTBD3
BAIS:6210Data Leadership and Management TBD3
BIOS:4150 (NEW)Data Science Foundations in RTBD3
BIOS:6721Machine Learning for Biomedical DataTBD3
CS:4310 Design and Implementation of AlgorithmsTBD3
CS:4400Database Systems   TBD3
CS:4420Artificial IntelligenceTBD3
CS:4470Health Data Analytics TBD3
CS:5110Introduction to InformaticsTBD3
CS:5430Machine LearningTBD3
CS:5630Cloud Computing TechnologyTBD3
DATA:3210/STAT:3210Experimental Design and AnalysisFall3
DATA:4890Data Science PracticumFall3
MATH:4840Mathematics of Machine LearningTBD3
STAT:4520  Bayesian Statistics  Fall3
STAT:4560Statistics for Risk Modeling IFall3
STAT:5810Research Data ManagementFall3
STAT:6530Environmental and Spatial StatisticsEvery other fall3
STAT:6550Introductory Longitudinal Data AnalysisFall3
STAT:6560Applied Time Series AnalysisEvery other spring3

Year 1 fall semester

  • STAT:3120 Probability and Statistics
  • STAT:3200 Applied Linear Regression
  • STAT:4540 Statistical/Machine Learning
  • STAT:5400 Statistical Computing

Year 1 spring semester

  • DATA:4750 Probabilistic Statistical Learning
  • STAT:4580  Data Visualization and Data Technologies

Year 2 fall semester

  • DATA:5890 MS Data Science Practicum*
  • DATA:4600 Casual Inference for Data Science

Year 2 spring semester

  • DATA:6220  Data Consulting and Communication
  • DATA:7400 Computer Intensive Statistics

*Students may substitute DATA:5890 by an appropriate internship/summer work experience, with approval by the course instructor.

Probability and Statistics (DATA/STAT:3120, 4 semester hours)

Basic concepts of probability, statistical models, discrete and continuous random variables and their distributions, expectations, conditional expectations, estimation of parameters, testing statistical hypotheses.

Applied Linear Regression (DATA/STAT:3200, 3 semester hours)

Regression analysis with focus on applications; model formulation, checking, selection; interpretation and presentation of analysis results; simple and multiple linear regression; logistic regression; ANOVA; hands-on data analysis with computer software.

Statistical Learning (DATA/STAT:4540, 3 semester hours)

Introduction to supervised and unsupervised statistical learning, with a focus on regression, classification, and clustering; methods will be applied to real data using appropriate software; supervised learning topics include linear and nonlinear (e.g., logistic) regression, linear discriminant analysis, cross-validation, bootstrapping, model selection, and regularization methods (e.g., ridge and lasso); generalized additive and spline models, tree-based methods, random forests and boosting, and support-vector machines; unsupervised learning topics include principal components and clustering. Requirements: an introductory statistics course and a regression course. Recommendations: prior exposure to programming and/or software, such as R, SAS, and Matlab. 

Data Visualization and Data Technologies (DATA/STAT:4580, 3 semester hours)

Introduces common techniques for visualizing univariate and multivariate data, data summaries, and modeling results. Students will learn how to create and interpret these visualizations, and to assess effectiveness of different visualizations based on an understanding of human perception and statistical thinking.  Data technologies for obtaining and preparing data for visualization and further analysis will also be discussed. Students will also learn how to present their results in written reports and to use version control to manage their work.

Causal Inference for Data Science (DATA/STAT:4600, 3 semester hours)

Introduce methods for reasoning about causes, effects, and bias when analyzing experimental and observational data. Topics include the potential outcomes framework, counterfactuals, confounding, and missing data; the identification and estimation of causal effects via propensity score methods, marginal structural models, instrumental variables, and directed acyclic graphs; as well as applications of machine learning and Bayesian methods to causal inference. Pre-requisites: (DATA:3120 or STAT:3120) and (DATA:3200 or STAT:3200).

Computing in Statistics (DATA/STAT:5400, 3 semester hours)

Python, R; database management; graphical techniques; importing graphics into word-processing documents (e.g., LaTeX); creating reports in LaTeX; SAS; simulation methods (Monte Carlo studies, bootstrap, etc.). 

Probabilistic Statistical Learning (DATA/STAT:4750, 3 semester hours)

This course focuses on essential machine learning and statistics ideas that are critical in analyzing modern complex and large data. Selected topics are covered in supervised learning: linear models, deep neural networks, and non-parametric models. Besides supervised learning, essential topics from non-linear dimension reduction, clustering, and recommender systems are part of the course.

MS Data Science Practicum  (DATA:5890 –1 course totaling 2 semester hours) Master’s second-year core courses

A sequence of bootcamps each fall concluded by a datathon event. Past offerings can be found in the links below. The 2024 data bootcamp will be available soon!

2023: https://stat.uiowa.edu/news/2023/11/2023-datathon-recap

Computer Intensive Statistics (DATA/STAT:7400, 3 semester hours)

This course is intended primarily for MS and PhD students in Statistics and Data Science to provide an introduction to a range of computationally intensive methodology and to the use of computation in statistical research and practice. The primary computing framework we will use is R; if you are not already familiar with R from your other courses it would be a good idea to take some time over break to become familiar with it. The R web site has links to a number of free introductions and to introductory books that are available.

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