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

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

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.