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Statistics Courses for Graduate Students in Other Disciplines

Which Statistics Courses are Right for You?

The Department of Statistics offers a variety of graduate courses which broadly fall under the following categories:

Please click on the course-category links above to toggle to the type of courses you are interested in taking. These are intended for graduate students outside of our discipline who want to learn a little or a lot of statistics not only to solidify their understanding of statistics and data science, but also to help them design their own research studies, analyze their data, or just understand the scientific literature more thoroughly. Many of these are so-called graduate service courses as well as other courses that are taken by our own students, which may be of interest to students in other fields who have learned enough introductory material to be ready for more advanced statistical methodology.

Please note that some of the courses listed under various categories are high demand courses and, therefore, you will need permission of the department to register for these courses. If you have any questions or need clarifications, please contact the Graduate Coordinator, Dr. Liang Liu.

For additional details about the courses, please click: UGA Bulletin Statistics courses

Basic Statistical Methods (6000-level)

STAT 6210 (3 hours): Introduction to Statistical Methods I. This is the first course on statistics emphasizing applications in social, behavioral sciences. Covers elementary topics, one- and two- sample inference, simple linear regression, some categorical data analysis. Uses a statistical software. Provides preparation for Introduction to Statistical Methods II.  

 

STAT 6220 (3 hours): Introduction to Statistical Methods II. A continuation of Introduction to Statistical Methods I. Introduces additional statistical methods not covered in the first course. Emphasizes applications in the social and behavioral sciences. Topics include inference for categorical variables, multiple regression, logistic regression, one-way ANOVA, two-way ANOVA, ANCOVA, and nonparametric methods. Uses a statistical software.

 

STAT 6230 (3 hours): Applied Regression Analysis. Applied methods in regression analysis with implementation in R. Topics include linear regression with mathematical examination of model assumptions and inferential procedures; multiple regression and model building, including collinearity, variable selection and inferential procedures; ANOVA as regression analysis; analysis of covariance; diagnostic checking techniques; generalized linear models, including logistic regression.

 

STAT 6315 (4 hours): Statistical Methods for Researchers. This is essentially a one-semester version of STAT 6210 and STAT 6220 that covers basic statistical methods through one- and two-sample inference, regression, correlation, one-way analysis of variance, analysis of covariance, and simple methods of categorical data analysis. Course emphasizes implementation and interpretation of statistical methods. Statistical software is integrated into the course.

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Modern and Advanced Statistical Software Programming (6000-level)

STAT 6360 and STAT 6360 E (3 hours): Statistical Software Programming. Programming techniques in modern statistical software, including SAS and R for students with some experience with computer programming. Topics include data input/output; data formats and types; data management; flow control, conditional execution, and program design; statistical graphics and exploratory data analysis; basic procedures, and functions for statistical modeling and inference. Note: Currently, we are not offering this course each year. Also, STAT 6360E is offered in Summer only.

 

STAT 6355 (3 hours): Advanced Statistical Programming. A second course in statistical computing, using the SAS programming language to read data, create and manipulate SAS data sets, writing and using SAS MACROS, and SAS programming efficiency. SAS-based implementation of Structured Query Language (SQL). Additional topics may include Hadoop and parallel computing.

 

STAT 6365 (3 hours): Modern Statistical Programming. Statistical analysis and data manipulation in R and Python. Implementation of SQL. Topics include data input/output; data formats and types; data management; functions for statistical modeling; introduction to algorithms; flow control and program design; and programs for complex data manipulation and analysis. Additional topics may include MATLAB and parallel computing.

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Topic-oriented Statistical Methods (6000-level)

STAT 6240 (3 hours): Sampling and Survey Methods. Design of finite population sample surveys. Stratified, systematic, and multistage cluster sampling designs. Sampling with probability proportional to size. Auxiliary variables, ratio and regression estimators, non-response bias. Uses a statistical software.

 

STAT 6250 (3 hours): Applied Multivariate Analysis and Statistical Learning. The methodology of multivariate statistics and machine learning for students specializing in statistics. Topics include inference on multivariate means, multivariate analysis of variance, principal component analysis, linear discriminant analysis, factor analysis, linear discrimination, classification trees, multi-dimensional scaling, canonical correlation analysis, clustering, support vector machines, and ensemble methods. Uses a statistical software.

 

STAT 6260 (3 hours): Statistical Quality Assurance. Basic graphical techniques and control charts. Experimentation in quality assurance. Sampling issues. Other topics include process capability studies, error analysis, SPRT, estimation and reliability. Uses a statistical software.

 

STAT 6270 (3 hours): Network Data Analysis and Graphical Models. Network structures are increasingly common across the sciences, such as brain connectivity, gene-gene interaction, protein-protein interaction, spread of diseases, social networks, etc. This course will introduce the state-of-the-art concepts and algorithms concerning networks in statistics and machine learning. The Presentation will entail a conscious balance of concepts, algorithms, and applications.

 

STAT 6280 (3 hours): Applied Time Series Analysis. Autoregressive, moving average, autoregressive-moving average, and integrated autoregressive-moving average processes, seasonal models, autocorrelation function, estimation, model checking, forecasting, and, if time permits, spectrum, spectral estimators. Uses a statistical software.

 

STAT 6290 (3 hours): Nonparametric Methods. Techniques and applications of nonparametric statistical methods, estimates, confidence intervals, one sample tests, two sample tests, several sample tests, tests of fit, nonparametric analysis of variance, correlation tests, chi-square test of independence and homogeneity, sample size determination for some nonparametric tests. Uses a statistical software.

 

STAT 6350 (3 hours): Applied Bayesian Statistics. Introduction to theory and methods of the Bayesian approach to statistical inference and data analysis. Covers components of Bayesian analysis (prior, likelihood, posterior), computational algorithms, and philosophical differences among various schools of statistical thought.

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Topic-oriented Statistical Methods (8000-level)

STAT 8040 (3 hours): Environmental Statistics: Methods for sampling the environment and analysis of environmental data are considered. Techniques are presented for estimation, hypothesis testing, and regression when data are non-normal and/or dependent. Statistical methods based on generalized linear models, linear mixed models, time series analysis, and spatial data analysis are surveyed from an applied perspective.

 

STAT 8090 (3 hours): Statistical Analysis of Genetic Data: Methods for analysis of genetic data, with an emphasis on gene mapping. Topics include quantitative genetics, covariance between relatives, estimation of genetic parameters, detection of genetic linkage in crosses and natural populations, association mapping, and QTL mapping. Emphasis on fitting models, estimating parameters, and making inferences based on genetic data.

 

STAT 8200 (3 hours): Design of Experiments for Research Workers: Methods for constructing and analyzing designed experiments are considered. Concepts of experimental unit, randomization, blocking, replication, and orthogonal contrasts are introduced. Designs include completely randomized design, randomized complete block design, Latin squares design, split-plot design, repeated measures design, and factorial and fractional factorial designs.

 

STAT 8210 (3 hours): Multivariate: Theory and Methods: An introduction to the theory and methodology of multivariate statistics for students with training in linear models and mathematical statistics. Topics include the multivariate normal distribution, one and two population inference on population mean vectors, MANOVA, principal component analysis, factor analysis, discrimination, classification, and canonical correlation.

 

STAT 8230 (3 hours): Applied Nonlinear Regression: Statistical modeling using nonlinear regression is considered. Topics include fixed-effects nonlinear regression models, nonlinear least squares, computational methods and practical matters, growth models, and compartmental models. Nonlinear mixed-effects models are discussed, including model interpretation, estimation and inference. Examples will be drawn from forestry, pharmaceutical sciences, and other fields.

 

STAT 8270 (3 hours): Spatial Statistics: Models and theories in spatial data, including geostatistics, lattice data, spatial point patterns, and space-time data. The course will focus on random field theory, various spatial regression models, model fitting, inferences and spatial prediction, with applications to agriculture, environmental sciences, forestry, and public health.

 

STAT 8290 (3 hours): Advances in Experimental Designs: Covers state-of-the-art knowledge on selected topics such as factorial experiments, fractional factorials, incomplete block designs, orthogonal arrays, crossover designs, response surface methodology, mixture experiments, optimal design theory for linear and nonlinear models, and design construction techniques.

 

STAT 8440 (3 hours): Statistical Inference for Bioinformatics: Concepts of statistical inference for students in the life sciences, including maximum likelihood, Bayesian inference, and stochastic modelling. The course focuses on Hidden Markov models, continuous time Markov chain (Poisson process, birth and death process, coalescent process), and their applications in modeling biological data. These topics will be mixed with applications of the statistical concepts to biological data. Emphasizes computer simulation over mathematical manipulation.

 

STAT 8460 (3 hours): Advanced Computational Biology and Bioinformatics: Development of computational methods to infer biological information from data, including DNA sequences, gene expression levels, epigenome, and microbiome data. Students will read research articles ranging from statistics to biology and conduct extensive data analysis. Focus on raw data processing as well as statistical learning methods for downstream analysis.

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Introductory and Advanced Statistical Collaboration (8000-level)

STAT 8000 (3 hours): Introductory Statistical Collaboration: Teaches students the communication skills necessary to successfully collaborate with non-statisticians in an interdisciplinary setting. Students will learn methods for conducting successful interactions with non-statisticians and will have opportunities to practice written and oral communication skills related to the application of statistics in other fields.

 

STAT 8001 (3 hours): Advanced Statistical Collaboration: Students will be matched with an active UGA researcher and be responsible for all aspects of a collaborative project with this researcher. In-class instruction will be provided to students on project management, presentation, and writing. Students will regularly present their progress, and the current literature on statistical consulting will be reviewed.

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Advanced Statistical Computing (8000-level)

STAT 8060 (3 hours): Statistical Computing I: Tools and methods of statistical computing beginning with mathematical and computational underpinnings of statistical computation and progressing through Monte Carlo simulation, numerical linear algebra, optimization, numerical differentiation and integration, and simulation-based statistical algorithms. Students will learn methods, theory, and implementation via existing functions and their own code.

 

STAT 8070 (3 hours): Statistical Computing II: Continuation of Statistical Computing I. Advanced statistical computing techniques will be covered. Topics may include advanced MCMC methods, Expectation-Maximization methods, machine-learning algorithms, constrained optimization, density estimation, nonparametric regression perfect sampling, data visualization, and parallel computing. Students will learn methods, theory, and implementation via existing functions and their own code.

 

STAT 8330 (3 hours): Advanced Statistical Applications and Computing: Advanced programming and implementation of modern statistical techniques using statistical software such as R. Topics include Monte Carlo simulations, resampling techniques, penalized regression, generalized linear models, robust methods, nonlinear regression, multiple testing adjustment, and smoothing techniques.

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Advanced Applied Statistics (6000/8000-level)

STAT 6420 (3 hours): Applied Linear Models: Introduction to data analysis via linear models and logistic regression. Linear regression topics include estimation, inference, variable selection, diagnostics, and remediation. Basic design of experiments, analysis of variance, and logistic regression will also be covered, including an introduction to generalized linear models. Vector and matrix formulations are used throughout the course.

 

STAT 6430 (3 hours): Design and Analysis of Experiments: Theory and methods for constructing and analyzing designed experiments are considered. Basic concepts in design of experiments, analysis of covariance, completely randomized designs, randomized complete and incomplete block designs, row-column designs, repeated measures designs, factorial designs, split-plot experiments will be covered. Additional topics may include response surface modeling, mixture designs.

 

STAT 8230 (3 hours): Applied Nonlinear Regression: Statistical modeling using nonlinear regression is considered. Topics include fixed-effects nonlinear regression models, nonlinear least squares, computational methods and practical matters, growth models, and compartmental models. Nonlinear mixed-effects models are discussed, including model interpretation, estimation and inference. Examples will be drawn from forestry, pharmaceutical sciences, and other fields.

 

STAT 8620 (3 hours): Categorical Data Analysis and Generalized Linear Models: Categorical data analysis and generalized linear models beginning with contingency tables and their analysis. Theory of generalized linear models will then be presented, followed by more detailed and application-oriented discussions of special cases, including logistic, log-linear models, and multinomial response models. Overdispersion is also discussed.

 

STAT 8630 (3 hours): Mixed-Effect Models and Longitudinal Data Analysis: Extensions of classical and generalized linear models with emphasis on longitudinal data analysis. Course will focus on linear mixed models, and marginal and mixed-effect versions of generalized linear models for longitudinal discrete data. Emphasis will be placed on the application of these models to analyze real data.

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Advanced Mathematical Statistics courses (6000/8000-level)

STAT 6510 (3 hours): Mathematical Statistics I: Concepts and basic properties of some special probability distributions, independence, moment generating functions, sampling distributions of statistics, limiting distributions.

 

STAT 6520 (3 hours): Mathematical Statistics II: Introduction to the fundamentals of statistical inference. Point estimation, including the properties of estimators and ways of evaluating or comparing them, confidence intervals, and hypothesis testing. Statistical inference in linear models, including regression and analysis of variance, is also discussed.

 

STAT 6530 (3 hours): Statistical Inference for Data Scientists: Mathematical and computational approaches to estimation and inference from frequentist and Bayesian perspectives. Sampling distributions; maximum likelihood estimation; computational maximization of likelihoods, including grid search, Newton- Raphson methods; likelihood ratio tests. Simulations of power and error rates. Introduction to Bayesian inference; prior and posterior distributions; model building; sampling from the posterior distribution; MCMC algorithms.

 

STAT 6810 (3 hours): Probability Distributions: Builds the foundation in probability distribution theory that is necessary to learn statistical inference. Emphasizes mathematical rigor and includes topics such as probability laws; random variables and probability distributions; joint, marginal and conditional distributions; expectation and conditional expectation; transformations; and properties of a random sample.

 

STAT 6820 (3 hours): Statistical Inference: The principles and theory behind statistical inference. It provides justification for many statistical procedures routinely used in practice and discusses principles and theory that can be used to develop reasonable solutions to new statistical problems.

 

STAT 8350 (3 hours): Bayesian Statistical Methodology with Applications: The theory and methodology of Bayesian statistical inference. Training in statistical modeling and data analysis under the Bayesian paradigm.

 

STAT 8530 (3 hours): Advanced Statistical Inference I: The theory of statistical inference is presented at an advanced level, including both frequentist and Bayesian perspectives. This course provides justification of many statistical procedures routinely used in good practice of statistics and discusses principles and theory that can be used to derive reasonable solutions to new statistical problems.

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