MS - Biostatistics and Data Science

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Course Descriptions

  • 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, linear regression, logistic regression and Poisson regression. Course content will be delivered through lectures, hands-on lab instruction and team-based learning using statistical packages including R, SAS and Stata. Traditional Lecture (3 credit 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 credit hours)

  • BDS XXX. Principles of Programming: 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 credits).

  • 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 credit hours)

  • BDS 723. Statistical Computation: This course is designed to 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. Students will learn descriptive statistics, graphical presentation, estimation (EM algorithm), and computational methods for optimization.  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 credit 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 credit 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 credit hours)

  • PHS 700. Essentials of Population Health Science.  Introduction to how the multiple determinants of health (e.g., health care, socioeconomic status, genetics, the physical environment and health behavior, and their interactions) have implications for the health outcomes of populations. Characteristics of populations defined by geography, diagnosis, and/or point of care will be discussed. Avenues in which health care systems, public health agencies, community-based organizations, retail health organizations work together to improve local, national, and global communities. Students will also learn how to view problems from a population health and population health management perspective. Descriptions of how clinical and non-clinical data is used to measure health-related outcomes, analyze patterns, communicate results, and develop evidence-based intervention practices to manage of health of populations will be explored. (3 credit hours)

  • BDS 725.  Survival Analysis. This course will give an overview of modern survival analysis methods. Topics included are survival functions, hazard functions, censoring and truncation, competing risks, estimation of survival and related functions, hypothesis testing and semi-parametric regression methods with survival data. Traditional Lecture (3 credit 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 credit hours)

  • BDS 792. Statistical Consulting: Provides hands-on training and experience in statistical consulting. Written and oral communication skills are emphasized. Ethical aspects of consulting are also discussed. Traditional Practicum/Internship (1 credit hour)

  •  MSCI 710

  •  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. Traditional Lecture (3 credit hours)

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

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

  • ID 709