PhD - Biostatistics and Data Science

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Course Descriptions

  • BDS 711. Statistical Methods in Research. Provides an introduction to selected important topics in statistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data types and analysis techniques. Specific topics include applications of statistical techniques such as point and interval estimation, hypothesis testing (tests of significance), correlation and regression, relative risks and odds ratios, sample size/power calculations and study designs. While the course emphasizes interpretation and concepts, there are also formulae and computational elements such that upon completion, class participants have gained real world applied skills. Traditional Lecture (3 hours)

  • BDS 712. Statistical Methods in Research II. A continuation of Statistical Methods in Research 1, this course introduces the student to more complicated methods than those discussed in the first course including generalized linear models, survival models and longitudinal data analysis. The emphasis will be on applied rather than theoretical statistics, and on understanding and interpreting the results of statistical analyses. Datasets will be analyzed using the statistical package STATA. This is a hands-on class with computer labs. Datasets will be analyzed under the supervision of instructors. Traditional Lecture (3 hours)

  • BDS 713. Intro to Data Management and Programming. Provides an introduction to programming and data management. The course will focus on planning and organizing programs to handle and process data, as well as the grammar of particular programming languages. Traditional Lecture (3 hours)

  • BDS 714. Statistical Methods for Clinical Trials. Provides a basic understanding of the statistical concepts important in the design, conduct and analysis of clinical trials. Traditional Lecture (3 hours)

  • BDS 715. Intro to Sample Survey Analyses. Provides an introduction to statistical concepts in the design and analyses of sample surveys. Covers topics such as instrument design, sampling procedures, variance estimation, reliability, validity, scaling and scoring, complex samples and weighting procedures. Traditional Lecture (3 hours)

  • BDS 721. Analytics. Provides an introduction to basic statistical and data analytic methods. This course covers topics such as data archetypes; exploratory data analysis; basic statistical paradigms including frequentist, likelihood and Bayesian approaches; contingency tables; sampling distributions; the Central Limit Theorem; point and interval estimation; sufficiency; tests of statistical significance including large sample, likelihood ratio and resampling approaches; basic random variable linear combinations; ANOVA; correlation; and linear, logistic, and Poisson regression. Course content will be delivered through lectures, hands-on lab instruction and team-based learning using multiple statistical packages (R, SAS and Stata). Traditional Lecture (3 hours)

  • BDS 722. Advanced Analytics. Continues introductions to intermediate and advanced statistical analysis methods for biomedical research. This course covers advanced regression topics, generalized linear models (GLM), generalized additive models (GAM), splines and smoothing techniques, decision trees, basic survival models, and introduces machine learning techniques (clustering, classification, regularization/penalized regression, feature selection, Bayesian methods, and unbiased estimators). Course content will be delivered through lectures and hands-on lab instruction. Traditional Lecture (3 hours)

  • BDS 723. Statistical Programming with R. This course will provide students with an introduction to statistical computing. Students will learn the core ideas of programming — functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction — through writing code to assist in numerical and graphical statistical analyses. This course will emphasize the learning of statistical methods and concepts through hands-on experience with real data. Since code is also an important form of communication among scientists, students will learn how to comment and organize code. Traditional Lecture (3 hours)

  • BDS 724. Longitudinal and Multilevel Models. Covers statistical models for drawing scientific inferences from clustered\correlated data such as longitudinal and multilevel data. Topics include longitudinal study design; exploring clustered data; linear and generalized linear regression models for correlated data, including marginal, random effects, and transition models; and handling missing data. Online, Internet, or Web-based Lecture (3 hours)

  • BDS 725. Survival Analysis. This course introduces basic concepts and methods for analyzing survival time data obtained from following individuals until occurrence of an event or their loss to follow-up. We will begin this course from describing the characteristics of survival (time to event) data and building the link between distribution, survival, and hazard functions. After that, we will cover non-parametric, semi-parametric, and parametric models and two-sample test techniques. In addition, we will also demonstrate mathematical and graphical methods for evaluating goodness of fit and introduce the concept of dependent censoring/competing risk. During the class, students will also learn how to use SAS to analyze survival data. Traditional Lecture (3 hours)

  • BDS 726. Generalized Linear Models. Provides a foundation in the theory and application of generalized linear models and related statistical topics. A generalized linear model (GLM) is characterized by (1) a response variable with a distribution in an exponential dispersion family and (2) a mean response related to linear combinations of covariates through a link function. GLMs allow a unified theory for many of the models used in statistical practice, including normal theory regression and ANOVA models, many categorical data models including logit and probit models for binary data, loglinear models, and models for gamma responses and survival data. Traditional Lecture (3 hours)

  • BDS 727. Nonparametric Analyses. Provides an introduction to modern topics in nonparametric data analysis for estimation and inference. Topics include kernel estimation, rank based methods, nonparametric regression, confidence sets and random processes. Methodology and theory are presented together. Traditional Lecture (3 hours)

  • BDS 728. Multivariate Analysis. Provides an introduction of the analysis of multivariate data, balancing theory, implementation and translation of these methods. Topics covered include matrix computations, visualization techniques, the multivariate normal distribution, MANOVA, principal components analysis, factor analysis, and other clustering techniques. Traditional Lecture (3 hours)

  • BDS 739. Computational Statistics. This course will cover efficient methods for obtaining numerical solutions to statistical problems. Topics include numerical optimization in statistical inference [expectation-maximization (EM) algorithm, Fisher scoring, etc.], Monte Carlo methods, random number generation, jackknife methods, bootstrap methods, kernel density estimation, and splines. Traditional Lecture (3 hours)

  • BDS 741. Statistical Inference I. Introduces probability and distribution theory, including axioms of probability; random variables; probability mass and density functions; common discrete and continuous distributions; transformations and sums of random variables; expectations, variances, and moments; hierarchical models and mixture distributions; and properties of random samples. Traditional Lecture (3 hours)

  • BDS 742. Statistical Inference II. This course is a continuation of Statistical Inference I and continues to introduce modern statistical theory and principles of inference based on decision theory and likelihood (evidence) theory. Traditional Lecture (3 hours)

  • BDS 743. Theory of Linear Models. Provides an introduction to the development and use of general linear models including frameworks for parameter estimation and inference in a variety of settings. Theoretical foundations of the models will be reinforced with areas in which the models are applied to answer scientific questions. Topics covered include matrix algebra, distribution theory for quadratic forms of normal random vectors, properties of OLS estimators, estimable functions and related themes. Traditional Lecture (3 hours)

  • BDS 750. Study Design. This course will equip doctoral-level biostatisticians and data scientists with the skills necessary to participate in the planning and analysis of biomedical, clinical, and population-based health studies. This course will cover a wide array of study designs, one and two-way classifications, nesting, blocking, factorial designs, multiple comparisons, confounding, power, sample size, and selected issues (randomization, blindness, adherence, dropout, phases) from clinical trials. Traditional Lecture (3 hours)

  • BDS 751. Statistical Inference in Genetics. This course will present fundamental theoretical concepts and statistical inference with emphasis on genetic epidemiology research for common human diseases. Five modules will be covered, including an introduction to statistical inference methods used on genetic data, familial aggregation methods, segregation analysis, linkage analysis, and testing associations between genetic variants and disease. Traditional Lecture (3 hours)

  • BDS 752. Advanced Statistical Genetics. An advanced course on modeling and methodology in statistical genetics for human diseases and traits. The course will cover topics including linkage analysis, population structure and stratification, admixture mapping, heritability and genetic risk prediction, familial aggregation, association analysis and others. On successful completion, participants will have the skills to develop and apply statistical methods towards a variety of genetic questions. Traditional Lecture (3 hours)

  • BDS 753. Bioinformatics. Provides an introduction to selected important topics in bioinformatics. The course focuses on integrating bioinformatics resources with basic biology and clinical applications to enhance population health research. Includes methods for the analysis of high-throughput next-generation sequence data and an introduction to the use of bioinformatics databases in precision medicine and population health. Covers common programs and algorithms for sequence alignment, evolutionary tree construction, database searching, functional interpretation of expressed genes, and identifying genetic mutations for human disease. Traditional Lecture (3 hours)

  • BDS 754. Principles of Programming with Python. This course will introduce fundamental programming concepts such as data structures and algorithms, object oriented programming, and the basics of building interactive applications in the python programming language. Traditional Lecture (3 hours)

  • BDS 761. Data Science. Provides a modern introduction to data science, including data wrangling and dynamic data visualization processes, while reinforcing reproducible research and applied statistical methods. Course content will be delivered through lectures and hands-on lab instruction. Traditional Lecture (3 hours)

  • BDS 762. Advanced Data Science. Provides a continuation into advanced Data Science topics with deeper programming and additional concepts. Topics include simulation, bootstrap, prediction, machine learning, and tool development. Course content will be delivered through lectures and hands-on lab instruction. Traditional Lecture (3 hours)

  • BDS 763. Database Systems. Review of database systems with special emphasis on data description and manipulation languages; data normalization; functional dependencies; database design; data integrity and security; distributed data processing; design and implementation of a comprehensive project. Traditional Lecture (3 hours)

  • BDS 764. Data Visualization. Provides an introduction to principles and techniques for creating effective interactive visualizations of quantitative information. Primary topics include principles for designing effective visualizations and implementing interactive visualizations using web-based frameworks. Traditional Lecture (3 hours)

  • BDS 765. Advanced Machine Learning. This course introduces students to the basic theories, concepts, and techniques of machine learning and gives them a glimpse of the state-of-the-art methods in this area. Topics covered include Bayesian estimation and decision theory, maximum likelihood estimation, nonparametric techniques, linear discriminant analysis, computational learning theory, support vector machines and kernel methods, boosting, clustering, dimensional reduction, and deep learning. Traditional Lecture (3 hours)

  • BDS 766. Advanced Computational Methods. Provides a blend of software engineering, stochastic processes and optimization for creating and deploying efficient analytic tools. Topics covered include software engineering paradigms, robust software design, data structure, object oriented design, parallel computations, and distributed computing, with a focus on implementation. Traditional Lecture (3 hours)

  • BDS 791. Special Topics. This course is intended to meet special needs of individual students. Students who wish to learn more about a particular topic can approach a mentor to determine an advanced course of study for that topic. The structure of an individual course is decided upon by the course director with approval from the curriculum committee. Traditional Independent Study (1-9 hours)

  • BDS 792. Statistical Consulting. Provides hands-on training and experience in statistical consulting. Written and oral communication skills are emphasized, working with prospective collaborators and ethical aspects of consulting are discussed. Traditional Practicum/Internship (1 hour)

  • BDS 793. Seminar Series: Microtopics. This course consists of attending the weekly Department of Data Science faculty seminar series. The goal of this seminar course is to expose students to current research topics in the field, to also give them exposure to seminar presentations, and to offer further detail into faculty research areas to assist in proposing a dissertation topic and research mentor. Traditional Lecture (1 hour)

  • BDS 794. Journal Club. This biweekly journal club will include student presentations of high-impact or seminal biostatistics, data science, or genomics journal articles. Each participating student will be required to present once per semester, with additional presentations by non-registered students, faculty, and staff. Traditional Lecture (1 hour)

  • BDS 796. Directed Research. Provides students to the opportunity to conduct research under the guidance of a faculty member from the Department of Data Science. Traditional Laboratory (3 hours)

  • BDS 797. Biostatistics & Data Science Internship. A work experience conducted in the Department of Data Science, an affiliated department, center, or institute at the University of Mississippi Medical Center, or a public or private organization. The internship is focused on the development of real world analytic, programming, and communication skills. Traditional Practicum/Internship (1-9 hours)

  • BDS 798. Dissertation Research. Research and preparation of a dissertation. Traditional Dissertation (1-9 hours)