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 credit 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 credit 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 credit 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 credit hours)
BDS 721. Analytics
Provides an introduction to basic statistical and data analytic methods. This course covers topics such as probabilities, contingency tables, exploratory data analysis, basic statistical paradigms, sampling distributions, point and interval estimation, tests of statistical significance including large sample and resampling approaches, ANOVA, correlation, linear regression, and logistic regression. Course content will be delivered through lectures and hands-on lab instruction. Traditional Lecture (3 credit hours)
BDS 722. Advanced Analytics
Continues introductions to intermediate and advanced statistical analysis methods for biomedical research. This course covers advanced regression topics, splines and smoothing techniques, Generalized Linear Models (GLM), Generalized Additive Models (GAM), decision trees, basic survival models, and basic approaches for clustering. Course content will be delivered through lectures and hands-on lab instruction. Traditional Lecture (3 credit hours)
BDS 723. Statistical Computation
Provides an introduction to computational methods employed in biostatistical and data science analyses. A mix of theory and applications, concepts such as numerical solvers, efficient programming, matrix manipulations, random variable generation, maximum likelihood estimation, randomization tests, bootstrapping and simulation will be introduced through targetted programming assignments that students complete in one or more statistical programming languages. Traditional Lecture (3 credit 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. Traditional Lecture (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 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 credit 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 credit 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 credit hours)
BDS 741. Statistical Inference I
Introduces probability and inference, including random variables; probability distributions; transformations and sums of random variables; expectations, variances, and moments; properties of random samples; and hypothesis testing. Traditional Lecture (3 credit 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 credit 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 credit hours)
BDS 751. Statistical Inference in Genetics
This course will present fundamental theoretical concepts and statistical inferential methods applied in genetic epidemiology research of complex human diseases. Traditional Lecture (3 credit 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 credit 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 research in population health. Includes methods for analysis of high-throughput NGS data, understanding bioinformatics databases in precision medicine and population health. Covers common programs and algorithms for sequence alignment, evolutionary tree construction, database searching, functional interpretation for expressed genes, and finding mutations in DNA for human disease. 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 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 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 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 credit hours)
BDS 765. Advanced Machine Learning
Provides an introduction to machine learning and statistical learning. Topics include: Supervised learning, unsupervised learning, additive and tree related models and methods to tackle high dimensional problems. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building novel tools to help enhance understanding of available data and use it to make informative choices. Traditional Lecture (3 credit 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 credit 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 a particular topic. The structure of an individual course is decided upon by the individual course instructor with approval from the program committee. Traditional Independent Study (1 - 9 credit 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 (3 credit hours)
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 credit hours)
BDS 797. Biostatistics & Data Science Internship
A program of work and study within the Department of Data Science, Center of Biostatistics & Bioinformatics, affiliated Departments, Centers or Institutes at the University or in an alternative public or private sector centered upon the development of real world skills and experiences with a culminating significant project. Traditional Practicum/Internship (1 - 9 credit hours)
BDS 798. Dissertation Research
Research and preparation of a dissertation. Traditional Dissertation (1 - 9 credit hours)
