## Course List

Credits 3. 3 Lecture Hours
Version control via Git and Github; code profiling; numerical optimization; writing documentation; creation of R packages; case studies of computational challenges based on modern machine learning methods including regularized logistic regression, k-means clustering, sparse

*Prerequisites: Graduate classification or approval of instructor.*
Credits 4. 3 Lecture Hours. 2 Lab Hours
For students in engineering, physical and mathematicalIntroduction to probability, probability distributions and statistical inference; hypotheses testing; introduction to methods of analysis such as tests of independence, regression, analysis of variance with some consideration of planned

*Prerequisite: MATH 152 or MATH 172.*
Credits 3. 3 Lecture Hours
Programming languages, statistical software and computing environments; development of programming skills using modern methodologies; data extraction and code management; interfacing lower-level languages with data analysis software; simulation; MC integration; MC-MC procedures; permutation tests;

*Prerequisite: STAT 612 and STAT 648.*
Credits 3. 3 Lecture Hours
Theory of estimation and hypothesis testing; point estimation, interval estimation, sufficient statistics, decision theory, most powerful tests, likelihood ratio tests, chi-square

*Prerequisite: STAT 610 or equivalent.*
Credits 3. 3 Lecture Hours
Matrix algebra for statisticians; Gauss-Markov theorem; estimability; estimation subject to linear restrictions; multivariate normal distribution; distribution of quadratic forms; inferences for linear models; theory of multiple regression and AOV; random-and mixed-effects

*Prerequisite: Course in linear algebra.*
Credits 3. 3 Lecture Hours
Elements of likelihood inference; exponential family models; group transformation models; survival data; missing data; estimation and hypotheses testing; nonlinear regression models; conditional and marginal inferences; complex models-Markov chains, Markov random fields, time series, and point

*Prerequisite: STAT 612.*
Credits 3. 3 Lecture Hours
Probability and measures; expectation and integrals, Kolmogorov's extension theorem; Fubini's theorem; inequalities; uniform integrability; conditional expectation; laws of large numbers; central limit theorems

*Prerequisite: STAT 610 or its equivalent.*
Credits 3. 3 Lecture Hours
Survey of the theory of stochastic processes; includes countable-state Markov processes, birth-death processes, Poisson point processes, renewal processes, Brownian motion and diffusion processes and covariance-stationary processes; theoretical development and applications to real world

*Prerequisites: STAT 610; MATH 409.*
Credits 3. 3 Lecture Hours
Core methods from traditional multivariate analysis and various extensions; probability distributions of random vectors and matrices, multivariate normal distributions, model assessment and selection in multiple regression, multivariate regression, dimension reduction, linear discriminant analysis, logistic discriminant analysis, cluster analysis, multidimensional scaling and distance geometry, and correspondence

*Prerequisite: STAT 611, STAT 630, STAT 650, or equivalent.*
Credits 3. 3 Lecture Hours
Second course in statistical machine learning; recursive partition and tree-based methods, artificial neural networks, support vector machines, reproducing kernels, committee machines, latent variable methods, component analysis, nonlinear dimensionality reduction and manifold learning, matrix factorization and matrix completion, statistical analysis of tensors and multi-indexed

*Prerequisites: STAT 612, STAT 613, and STAT 616.*
Credits 3. 3 Lecture Hours
Review of basic concepts and important convergence theorems; elements of decision theory; delta method; Bahadur representation theorem; asymptotic distribution of MLE and the LRT statistics; asymptotic efficiency; limit theory for U-statistics and differential statistical functionals with illustrations from M-,L-,R-estimation; multiple

*Prerequisite: STAT 614.*
Credits 3. 3 Lecture Hours
Conditional expectation; stopping times; discrete Markov processes; birth-death processes; queuing models; discrete semi-Markov processes; Brownian motion; diffusion processes, Ito integrals, theorem and limit distributions; differential statistical functions and their limit distributions; M-,L-,R-

*Prerequisite: STAT 614 or STAT 615.*
Credits 3. 3 Lecture Hours
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

*Prerequisites: Knowledge of R, Fortran, or C.*
Credits 3. 3 Lecture Hours
Nonparametric function estimation; kernel, local polynomials, Fourier series and spline methods; automated smoothing methods including cross-validation; large sample distributional properties of estimators; recent advances in function

*Prerequisite: STAT 611.*
Credits 3. 3 Lecture Hours
Basic probability theory including distributions of random variables andIntroduction to the theory of statistical inference from the likelihood point of view including maximum likelihood estimation, confidence intervals, and likelihood ratioIntroduction to Bayesian

*Prerequisites: MATH 221, MATH 251, and MATH 253.*
Credits 3. 3 Lecture Hours
Regression and the capital asset pricing model, statistics for portfolio analysis, resampling, time series models, volatility models, option pricing and Monte Carlo methods, copulas, extreme value theory, value at risk, spline smoothing of term

*Prerequisites: STAT 610, STAT 611, STAT 608.*
Credits 3. 3 Lecture Hours
Decision theory; fundamentals of Bayesian inference; single and multi-parameter models; Gaussian model; linear and generalized linear models; Bayesian computations; asymptotic methods; non-iterative MC; MCMC; hierarchical models; nonlinear models; random effect models; survival analysis; spatial

*Prerequisite: STAT 613.*
Credits 3. 3 Lecture Hours
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

*Prerequisites: STAT 630, or STAT 610 and STAT 611; MATH 304.*
Credits 3. 3 Lecture Hours
Uncertainty regarding parameters and how they can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution; emphasis on modeling and computations under the Bayesian paradigm; includes prior distributions, Bayes Theorem, conjugate and non-conjugate models, posterior simulation via the Gibbs sampler and MCMC, hierarchical

*Prerequisites: STAT 630, or equivalent or approval of instructor.*
Credits 3. 3 Lecture Hours
An application of the various disciplines in statistics to data analysis, introduction to statistical software; demonstration of interplay between probability models and statistical

*Prerequisite: Concurrent enrollment in STAT 610 or approval of instructor.*
Credits 3. 3 Lecture Hours
Design and analysis of experiments; scientific method; graphical displays; analysis of nonconventional designs and experiments involving categorical

*Prerequisite: STAT 641.*
Credits 3. 3 Lecture Hours
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

*Prerequisites: STAT 630, STAT 652, STAT 641, STAT 642, or STAT 611; prior knowledge of matrices and R programming.*
Credits 3. 3 Lecture Hours
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

*Prerequisites: STAT 604, STAT 651, STAT 652 or equivalent or prior approval of instructor.*
Credits 3. 3 Lecture Hours
Background to conduct research in the development of new methodology in appliedTopics covered will include: exploratory data analysis; sampling; testing; smoothing; classification; time series; and spatial data

*Prerequisite: Approval of instructor.*
Credits 3. 3 Lecture Hours
Develop communication skills in teaching, research and statistical consulting; classroom and group exercises, teaching best practices; using simulations in the classroom, techniques to foster active learning environments; developing consulting techniques; communicating research

*Prerequisites: Graduate classification in statistics or approval of instructor.*
Credits 3. 3 Lecture Hours
Introduction to both probability and statistics with emphasis on applications in data science; topics include basic probability concepts, sample space, conditional probability, random variables, as well as statistical

*Prerequisites: MATH 411 or STAT 414; graduate classification or approval of the instructor.*
Credits 3. 3 Lecture Hours
For graduate students in other disciplines; non-calculus exposition of the concepts, methods and usage of statistical data analysis; T-tests, analysis of variance and linear

*Prerequisite: MATH 102 or equivalent.*
Credits 3. 3 Lecture Hours
Advanced topics in ANOVA; analysis of covariance; and regression analysis including analysis of messy data; non-linear regression; logistic and weighted regression; diagnostics and model building; emphasis on concepts; computing and

*Prerequisite: STAT 652.*
Credits 3. 3 Lecture Hours
Aspects of numerical analysis for statisticians and data scientists including matrix inversion, splines, function optimization and MCMC; emphasis on implementing methods in R and python; data science skills such as code profiling, web scraping and data

*Prerequisites: Basic knowledge of R or Python.*
Credits 3. 3 Lecture Hours
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

*Prerequisite: STAT 408 or equivalent.*
Credits 3. 3 Lecture Hours
Programming with SAS/IML, programming in SAS Data step, advanced use of various SAS

*Prerequisites: STAT 604.*
Credits 3. 3 Lecture Hours
Examination of aspects of semiparametric regression, especially involving generalized linear models such as logistic regression, and inclusion of completely nonparametric regression, partially linear models, additive models and grouped data including longitudinal data; topics include shape constraints, spatial models, robustness and accounting for missing

*Prerequisites: STAT 408 or STAT 608, or equivalent.*
Credits 3. 3 Lecture Hours
Advanced wavelet-based algorithms designed for summarization of large and noisy data sets for subsequent statistical modeling and learning; theoretical component providing unified multiresolution-based framework for efficient modeling, synthesis, analysis, and processing of broad classes of signals and images; applications in geosciences, biomedical signal processing, signal and image denoising, medical diagnostics, financial data

*Prerequisite: Familiarity with computing in MATLAB, Octave, Python; STAT 627 or approval of instructor.*
Credits 1 to 3. 1 to 3 Lecture Hours
Review of the fundamental concepts and techniques of statistics; topics included in Advanced Placement Statistics; exploring data, planning surveys and experiments, exploring models, statistical

*Prerequisite: Approval of instructor.*
Credits 3. 3 Lecture Hours
Introduction to diverse modes of analysis now available to solve for univariate time series; basic problems of parameter estimation, spectral analysis, forecasting and model

*Prerequisite: STAT 611 or equivalent.*
Credits 3. 3 Lecture Hours
Spatial statistics from an advanced perspective; Gaussian processes; Gaussian Markov random fields; positive definite functions; nonstationary and multivariate process; hierarchical spatial models; measurement error; change of support; computational approaches for large spatial data sets; spatio-temporal

*Prerequisites: STAT 612, STAT 613, and STAT 632.*
Credit 1. 1 Lecture Hour.
Oral presentations of special topics and current research inMay be repeated for

*Prerequisite: Graduate classification in statistics.*
Credits 3. 3 Lecture Hours
Application of data science methods including machine learning to research problems; team project-based training for project management, interdisciplinary collaboration and communication

*Prerequisite: Two or more of CSCE 633, CSCE 636, CSCE 666, CSCE 676, ECEN 758, ECEN 649, ECEN 740, ECEN 743, ECEN 765, ECEN 760, STAT 616, STAT 618 or STAT 639; Python programming experience is highly recommended. Cross Listing: CSCE 725 and ECEN 725.*
Credits 1 to 3. 1 to 3 Other Hours
Practicum in statistical consulting for students in PhDStudents will be assigned consulting problems brought to the Department of Statistics by researchers in other

*Prerequisite: STAT 642 or its equivalent.*
Credits 1 to 6. 1 to 6 Other Hours
Individual instruction in selected fields in statistics; investigation of special topics not within scope of thesis research and not covered by other formal

*Prerequisites: Graduate classification and approval of department head.*
Credits 1 to 4. 1 to 4 Lecture Hours
Selected topics in an identified area ofOpen to non-May be repeated for

*Prerequisite: Approval of instructor.*
Credits 1 to 23. 1 to 23 Other Hours
Research for thesis or

*Prerequisite: Graduate classification.*
Credits 2. 2 Lecture Hours
Application of various statistical methods, including but not limited to, experimental design, sampling and survey, graphics and tables and all sorts of modeling and data mining techniques, to solve real problems; communication with clients and identification of the statistical problems to be solved, outlining of a project plan for solving a statistical problem, use of proper statistical software or methodology needed for the problem; creation of a statistical report for the problemMay be repeated four times forMust be taken on a satisfactory/unsatisfactory

*Prerequisite: STAT 642 or equivalent.*
Credit 1. 1 Lecture Hour.
Provision of consulting service to researchers from various disciplines at Texas A&M; integration of statistical learning in class and application to real world problems; identification of clients' statistical problems; consideration and implementation of statistical procedures; effective communication with clients for interpretation of results and promotion of ethical guidelines in statisticalMay be repeated one time forMust be taken on a satisfactory/unsatisfactory

*Prerequisite: STAT 648.*
Credit 1. 1 Lecture Hour.
Familiarize the present status of research in a wide variety of new areas of statistical research; content will vary from semester to semester but will always be framed around introducing new researchMay be taken six times for

*Prerequisites: Graduate classification in the Department of Statistics or approval of instructor.*