Course Information

STAT 5024 - Effective Communication in Statistics (3 credits)

Communication skills necessary to be effective interdisciplinary statistical collaborators. Explaining and presenting statistical concepts to a non-statistical audience, helping scientists answer their research questions, and managing an effective statistical collaboration meeting.

STAT 5204G - Experimental Design: Concepts and Applications (3 credits)

Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Completely randomized, randomized complete block and Latin square designs, analysis of covariance, split-plot designs, factorial and fractional factorial designs, incomplete block designs, repeated measures, power and sample size, mean separation procedures.

STAT 5214G - Advanced Methods of Regression (3 credits)

Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression.

STAT 5615 - Statistics in Research I (3 credits)

Concepts in statistical inference, including basic probability, estimation, and test of hypothesis, point and interval estimation and inferences; categorical data analysis; simple linear regression; and one-way analysis of variance. 

STAT 5616 - Statistics in Research II (3 credits)

Multiple linear regression; multi-way classification analysis of variance; randomized block designs; nested designs; and analysis of covariance.

STAT 5904 - Project and Report (3 credits)

Preparation and final presentation of capstone project. 

STAT 5054 - Introduction to Statistical Computing (3 credits)

Introduction to modern programming packages for data analysis. Basics of coding, language syntax, and statistical functionality to read in raw data files and data sets, subset data, create variables, and recode data. Summaries in the form of tables and graphs. Data analysis using standard statistical methods and data management and analysis of large data sets. Parallel computing. Applied data analysis is emphasized rather than statistical theory. Pre: Graduate standing.

STAT 5154 - Statistical Computing in Data Analytics (3 credits)

Computational techniques for advanced applied statistical analyses and machine learning methods. Project management for larger data projects including computational constraints, pitfalls, and techniques related to different data types. Advanced report generation across different media, efficient R programming, advanced statistical function writing, parallel statistical computing with R, handling missing data, numerical optimization methods, the EM algorithm, and Monte Carlo methods. Pre: 5054. (3H, 3C).

STAT 5525 - Data Analytics I (3 credits)

Basic techniques in data analytics including the preparation and manipulation of data for analysis and the creation of data files from multiple and dissimilar sources. The data mining and knowledge discovery process. Overview of data mining algorithms in classification, clustering, association analysis, probabilistic modeling, and matrix decompositions. Detailed study of classification methods including tree-based methods, Bayesian methods, logistic regression, ensemble, bagging and boosting methods, neural network methods, use of support vectors and Bayesian networks. Detailed study of clustering methods including k-means, hierarchical and self-organizing map methods. 

STAT 5526 - Data Analytics II (3 credits)

Techniques in unsupervised and visualized learning in high dimension spaces. Theoretical, probabilistic, and applied aspects of data analytics. Methods include generalized linear models in high dimensional spaces, regularization, lasso and related methods, principal component regression (pca), tree methods, and random forests. Clustering methods including k-means, hierarchical clustering, biclustering, and model-based clustering will be thoroughly examined. Distance-based learning methods include multidimensional scaling, the self-organizing map, graphical/network models, and isomap. Supervised learning will consist of discriminant analyses, supervised pca, support vector machines, and kernel methods.