MS - Biostatistics and Data Science

Main Content

Course Descriptions

  • BDS 706. Ethics in Biostatistics & Data Science Research & Practice. This interactive course encompasses traditional elements of responsible conduct of research training, best practices in data management and analysis, and ethical issues encountered during the development and application of biostatistical and data science methods. Topics covered include research misconduct, protection of human subjects, data management, reproducibility of research, authorship, collaboration, conflicts of interest and commitment, peer review, and healthy mentoring relationships, with accompanying case studies relevant to the data science field. Emerging issues in clinical trials, data science, and artificial intelligence will be discussed. Guidelines published by professional organizations composed of statisticians and data scientists will be reviewed. Class sessions will consist of a traditional lecture portion where concepts and definitions are explained, followed by one or more case study discussions. (1 hour)

  • 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 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 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. (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 and Machine Learning 1. 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 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 765. Data Science and Machine Learning 2. 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 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 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)

ID 709, MSCI 710, PHS 703, and BDS-approved Electives course descriptions may be found in the current version of the UMMC Bulletin.