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
• Two electives (or one, if two are taken in year 2 fall)

Year 2 fall semester

• DATA:5890 MS Data Science Practicum*
• One elective (or two)

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

Probability and Statistics (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 (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 (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 (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.

Computing in Statistics (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: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.

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

Each student will be supervised by a faculty member to complete a project that solves a real‐world problem using knowledge gained from the core courses. Students are required to submit a written report and give an oral presentation of their projects; the written report must include the background and significance of the problem, analysis method, presentation and interpretation of the results including tables and visualization, discussion, and references, plus appendices comprising technical details and documentation of computer code used in the analysis. A capstone committee consisting of three faculty members will evaluate the capstone projects and assign the final grades (S or U), with inputs from the supervising faculty members.