This is a listing of the courses offered online.  Please refer to the actual TAMU schedule to confirm course availability.

If looking at courses on the TAMU course schedule, the online courses are listed as SECTION 70x.

Not all courses are offered each semester.  Some courses are recorded live and then ‘minded’ by a faculty member.  Minded courses use the same notes and lecture videos as the live semester. A faculty member will assign homework and administer exams.  Each course has a live Q&A session regardless if it is taught live or not.  If the course is minded, the faculty member who is minding the course will host the Q&A sessions.

Efficient uses of existing statistical computer programs; generation of random numbers; using and creating functions and subroutines; statistical graphics; programming of simulation studies; and data management issues.  Credit 3.

Planning, execution, and analysis of sampling from finite populations; simple, stratified, multistage, and systematic sampling; ratio estimates. Prerequisite: STAT 601 or 652 or concurrent enrollment in STAT 641. Credit 3.

Multiple, curvilinear, nonlinear, robust, logistic and principal components regression analysis; regression diagnostics, transformations, analysis of covariance. Prerequisite: STAT 601 or 641. 641 is a required prerequisite for MS STAT. Credit 3.

Machine learning methods related to multivariate data. Prerequisite:  STAT 630. Credit 3.

**This is a higher level course. You need to have done well in STAT 630.  Familiarity with multivariable calculus, linear algebra, probability theory and theory of statistical inference is needed.  Also, high level programming language – R/Python/Matlab etc…

Survey of common tools used by statisticians for high performance computing and big data type problems; shell scripting; HPC clusters; code optimization and vectorization; parallelizing applications using numerical libraries; open MP, MPI and parallel R; data management and revision control using Git; exploration of SQL, survey NOSQL databases; introduction to Python. Prerequisites: Knowledge of R, Fortran, or C. Credit 3.
Additional Information

Introduction to statistical time series analysis; autocorrelation and spectral characteristics of univariate, autoregressive, moving average models; identification, estimation and forecasting. Prerequisite: STAT 601 or 642 and a working knowledge of complex numbers and trigonometry.  Credit 3.
Additional Information

Basic probability theory including distributions of random variables and their expectations. Introduction to the theory of statistical inference from the likelihood point of view including maximum likelihood estimation, confidence intervals, and likelihood ratio tests. Introduction to Bayesian methods. Prerequisite: Three semesters of calculus, including multiple integration and a basic understanding of limits.  Credit 3.
Additional Information

This course is an applied statistical course that emphasizes on implementing financial and economic models with real data. Using the software in particular R for computation and analysis is essential.

Prerequisite: STAT 630, knowledge of vectors and matrices; probability, distributions and moments; maximum likelihood and
(generalized) least squares estimation; confidence intervals, hypothesis tests and linear regressions.  Credit 3.
Additional Information

Exploratory analysis of multivariate data using ordination and clustering techniques; supervised learning methods of predictive modeling; regression and classification; model selection and regularization; resampling methods; nonlinear and tree-based models; error rate estimation; use of R software. Prerequisites: STAT 630, or STAT 610 and STAT 611; MATH 304.Credit 3.

The objective of this applied master’s level course is to introduce students to the Bayesian paradigm for data analysis. Students learn how uncertainty regarding parameters can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution. Students are exposed to foundational principles, but the course emphasizes modeling and computations under the Bayesian paradigm. Prerequisites: STAT 630. Additionally, familiarity with R or other statistical software, training in vector/matrix algebra, and some exposure to linear regression will be highly beneficial.   Credit 3.

Broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, clustering and classification; applications to scientific and online data. 
Prerequisites: Familiarity with programming language R and knowledge of basic multivariate calculus, statistical inference, and linear algebra is expected. Students should be comfortable with the following concepts: probability distribution functions, expectations, conditional distributions, likelihood functions, random samples, estimators and linear regression models.
3 credits

An application of the various disciplines in statistics to data analysis, introduction to statistical software; demonstration of interplay between probability models and statistical inference. Prerequisites: Two semesters of Calculus, STAT 604, and STAT 630.
Credit 3.

Design and analysis of experiments; scientific method; graphical displays; analysis of non-conventional designs and experiments involving categorical data. Prerequisites: STAT 641. Credit 3.

Survey of crucial topics in biostatistics; application of regression in biostatistics;analysis of correlated data; logistic and Poisson regression for binary or count data; survival analysis for censored outcomes; design and analysis of clinical trials; sample size calculation by simulation; bootstrap techniques for assessing statistical significance; data analysis using R. Prerequisites: STAT 630, STAT 641, and STAT 642 or approval of current instructor. Credit 3.

An overview of relevant biological concepts and technologies of genomic/proteomic applications; methods to handle, visualize, analyze, and interpret genomic/proteomic data; exploratory data analysis for genomic/proteomic data; data preprocessing and normalization; hypotheses testing; classification and prediction techniques for using genomic/proteomic data to predict disease status. Prerequisites: STAT 604, 651, 652, or STAT 604, 641, 642, or equivalent or prior approval of instructor.
Credit 3.

Spatial correlation and its effects; spatial prediction (kriging); spatial regression; analysis of point patterns (tests for randomness and modelling patterns); subsampling methods for spatial data. Prerequisite: STAT 630 and STAT 608 or STAT 601 or equivalent. Credit 3.

Statistics in Research I. (3-0). Credit 3. For graduate students in other disciplines. A non-calculus exposition of the concepts, methods, and usage of statistical data analysis. T-tests, analysis of variance, and linear regression. Prerequisite: MATH 102 or equivalent.  Credit 3.

*Cannot be used towards the Master’s degree.  Can be used towards a certificate.

Continuation of STAT 651. Concepts of experimental design, individual treatment comparisons, randomized blocks and factorial analysis, multiple regression, chi-square tests and a brief introduction to covariance, non-parametric methods, and sample surveys. Prerequisite: STAT 651.   Credit 3.

*Cannot be used towards the Master’s degree.  Can be used towards a certificate.

The analysis of messy and complex data sets using analysis of variance, analysis of covariance and regression analysis. Transformations; regression diagnostics; nonlinear, robust, logistic and principal components regression; structural equations. Prerequisite: STAT 642 or 652. Credit 3.

Applied Analytics:  Introduction to data mining and will demonstrate the procedures; Optimal prediction decisions; comparing and deploying predictive models; neural networks; constructing and adjusting tree models; the construction and evaluation of multi-stage models. NOTE: For this course, you will be required to have a computer with good processing power.  Prerequisite: Course on multi linear regression (STAT 608) and an understanding of programming .  Credit 3.

Introduction to analysis and interpretation of categorical data using ANOVA/regression analogs; includes contingency tables, loglinear models, logistic regression; use of computer software such as R, GLIM, SPSSX. Prerequisite: STAT 601 or 641 or 652  Credit 3.
Additional Information

The course will cover many aspects of semiparametric regression, especially involving generalized linear models such as logistic regression, and including completely nonparametric regression, partially linear models, additive models and grouped data (including longitudinal data).  Prerequisite: STAT 408/608. Credits 3.

Individual instruction in selected fields in statistics; investigation of special topics not within scope of thesis research and not covered by other formal courses. Prerequisite: Passed at least 27 STAT credit hours with at least an overall 3.0 GPA. Credits 3.

Course content will vary by the semester. 
Credits 3.

Practicum in statistical consulting. Students will be assigned consulting problems brought to the Department of Statistics by researchers in other disciplines. Prerequisite: Passed at least 27 STAT credit hours with at least an overall 3.0 GPA.
Credit 2.