The MAS degree emphasizes practical methods in statistics, focusing on applications and computational aspects.
The goal of this degree is to enable students to start working as practicing statisticians in industry, government or academia immediately after graduation. This degree is offered via traditional resident instruction (on-campus), as well as online.
Download a printable flier with highlights of the MAS Online program. (Click here to download the pdf file.)
- Math Bootcamp: Mathematical Tools for Statisticians (0 credits). Intensive review of mathematical methods that will be used in the program, including but not limited to: differential and integral calculus, chain rule, l'Hôpital's rule, integration by parts, Taylor's theorem, multiple integrals, sequences and series, limits, linear algebra, matrix theory. Not for credit; students may test out.
- Computing Bootcamp: Statistical Computing Tools (0 credits). Software packages, graphics, and programming, using R, SAS, other popular packages. Not for credit; students may test out.
- STAA561: Probability with Applications (2 credits). Random variables, continuous and discrete distributions, expectations, joint and conditional distributions, transformations. Applications to capture/recapture, financial and industrial models. Prerequisite is Math Bootcamp or passing a competency exam.
- STAA551: Regression Models and Applications (2 credits). Estimation and hypothesis testing methods in linear models including t-tests, ANOVA, regression, including multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding. Implementation in SAS and R. Prerequisite is Computing Bootcamp or passing a competency exam.
- STAA572: Nonparametric Methods (2 credits). Rank-based methods, nonparametric inferential techniques, scatterplot smoothing, nonparametric function estimation. Environmental, bio- science applications. Prerequisites are STAA551 and STAA561.
- STAA566: Computational and Graphical Methods (1 credit). Exploratory data analysis using graphics, effective communication with graphs, data reduction methods. Prerequisite is Computing Bootcamp.
- STAA562: Mathematical Statistics (2 credits). Theory and applications of estimation, testing, and confidence intervals. Computer simulations; sampling from the normal distribution. Prerequisite is STAA561.
- STAA552: Generalized Regression Models (2 credits). Nonlinear regression, iteratively re-weighted least squares, dose-response models, count data, multi-way tables, survival analysis. Prerequisite is STAA551.
- STAA567: Methods in Simulation and Computation (1 credit). Sampling methods, simulating distributions of test statistics, optimization. Prerequisites are STAA561 and STAA552 or concurrent registration.
- STAA573: Analysis of Time Series (2 credits). Moving average and auto-regressive correlation structures, estimation and forecasting, modeling seasonality. Financial and environmental applications. Co-requisites are STAA551 and STAA561.
- STAA553: Experimental Design (2 credits). Analysis of variance, covariance, randomized block, latin square, split-plot, factorial, balanced and unbalanced designs. Applications to agriculture, biosciences. Implementation in SAS and R. Prerequisites are STAA551 and STAA562.
- STAA565: Quantitative Reasoning (1 credit). Confounding, types of bias such as selection bias and regression effect bias, Simpson’s paradox, experiments versus observational studies, etc. Co-requisite is STAA551.
- STAA571: Survey Statistics (2 credits). This course is the first half of our graduate-level class, including survey designs, simple random, stratified, and cluster samples. Estimation and variance estimation. Prerequisites are STAA551 and STAA562.
- STAA575: Applied Bayesian Statistics (2 credits). Bayesian analysis of statistical models, prior and posterior distributions, computing methods, interpretation. Prerequisites are STAA552, STAA562 and STAA 567.
- STAA554: Mixed Models (2 credits). Topics in linear, generalized linear, and nonlinear models with fixed and random predictors, balanced and unbalanced cases. Statistical topics with be integrated with the use of the computer packages SAS and R. Prerequisite is STAA553.
- STAA574: Multivariate Analysis (2 credits). Multivariate ANOVA, principal components, factor analysis, cluster analysis, discrimination analysis. Prerequisites are STAA551 and STAA561.
- STAA576: Methods in Environmental Statistics (2 credits). This course will introduce a number of statistical methodologies that are used in environmental and ecological studies. Students are introduced to topics in spatial statistics, and abundance estimation for biological populations. Prerequisites are STAA552 and STAA561.
- STAA577: Statistical Learning and Data Mining (2 credits). Regularization, prediction, regression, classification and clustering. Students will learn to implement modern statistical techniques for analyzing the types of data that would be encountered by statisticians working in business, medicine, science and government. Prerequisites: STAA551 and STAA561. Note: R programming skills (CSSA) are expected.
- STAA568: Topics in Industrial and Organizational Statistics (1 credit). Quality management, process control, reliability, decision making. Pre-requisites are STAA553 or concurrent registration and STAA562.
STAA556: Statistical Consulting (3 Credits). Consultant-client interactions, communications, ethical practices. Complete a consulting project and provide a report. Prerequisites is 28 credits of STAA coursework or instructor consent.
At least three semesters of calculus or through multivariate calculus
A course in linear algebra
At least one undergraduate-level statistics course
Math Entrance Exam (preferred) or GRE scores
Enrollment is limited
MAS Capstone Consulting
The MAS program culminates with the Capstone Consulting Module, STAA556. The course runs for six weeks (mid-May until the end of June). Enrollment in STAA556 requires successful completion of all previous 28 credits of the MAS program.
Students are assigned a genuine client-based project. Working in groups, students are expected to perform a principled and thorough statistical analysis of client data in response to some well-defined questions posed by a client. The results of the analysis are documented into a formal technical report which constitutes the main assignment of the course.
The module provides an authentic opportunity for students to apply and consolidate techniques and tools acquired over the preceding MAS courses.
Clients who have participated in the Capstone Consulting course include Elanco (a division of Eli Lilly and Company), United States Department of Agriculture, ChildSafe (a local non-profit organization) and University of Colorado Health.