BIOS 6030 INTRODUCTORY BIOSTATISTICS (3)
Prerequisites: None. Introduction to statistical methodology in the health field. Topics covered include presentation of data (graphs and tables), descriptive statistics, concepts of probability, estimation of parameters, hypothesis testing, simple linear regression, correlation, and the analysis of attribute data. The course provides students a firm foundation in statistical methods. see learning objectives
BIOS 6040 INTERMEDIATE BIOSTATISTICS (3)
Prerequisites: BIOS 6030 and at least one of BIOS 6230, BIOS 6240, BIOS 6280. This is a second course in applied biostatistics. It covers one-way and two-way analysis of variance, repeated measures designs, simple and multiple regression and correlation analyses, analysis of covariance, and logistic regression. see learning objectives
BIOS 6220 DATABASE MANAGEMENT IN THE HEALTH SCIENCES (3)
Prerequisites: None. Advanced programming techniques required in the maintenance of large data sets typical of health-related data systems, techniques of data file design, creation, maintenance, manipulation, updating, and retrieval for analysis by statistical packages. Methodology is illustrated with ACCESS, SAS, and SPSS. Utilities for communication between computers are discussed. see learning objectives
BIOS 6230, 6240 COMPUTER PACKAGES FOR STATISTICAL ANALYSIS (1 credit each)
Prerequisties: None. Use of statistical packages (6230: SAS; 6240: SPSS) and an introduction to mainframe computing with transmission of data. At least one course is strongly recommended for all students who take advanced statistical courses. see learning objectives
BIOS 6280 INTRODUCTION TO STATA (1)
Prerequisites: None. Offered only when needed, this course is an introduction to the statistical computing package, STATA. see learning objectives
BIOS 6300 INTRODUCTION TO ArcGIS (1)
Prerequisites: None. This course provides the foundation for becoming a successful ArcView®, ArcEditor™, or ArcInfo™ user. This course covers fundamental Geographic Information Systems (GIS) concepts as well as how to query a GIS database, manipulate tabular data, edit spatial and attribute data, and present data clearly and efficiently using maps and charts at the introductory level. Participants learn how to use ArcMap™, ArcCatalog™, and ArcToolbox™ and explore how these applications work together to provide a complete GIS software solution. The course materials authorized by Environmental Systems Research Institute (ESRI), the producer of ArcGIS software, are taught by ESRI-authorized instructors. A fee covering the course materials is required. Two forms of enrollment are available, either as a certificate enrollee or for credit (1 hour). The course is taught in several formats – once a week per period or as a two-day intercession course. see learning objectives
BIOS 6350 ENVIROMENTAL BIOSTATISTICS (3)
Prerequisites: BIOS 6030 and BIOS 6230. The objective of this course is the application of statistical methods to the collection and analysis of environmental data. The course is divided into three parts. Part 1 deals with field sampling designs along with methods used to estimate the mean, total amount, sampling errors of the mean and total amount as well as sample size and power calculations for each sampling design. Part 2 deals with a broad range of statistical techniques relating to environmental data. Part 3 deals with linking environmental data to various health indices. The focus will be on numerical computation and interpretation of results of statistical application using SAS see learning objectives
BIOS 6800 INTRODUCTION TO PUBLIC HEALTH GIS (3)
Prerequisites: BIOS 6030 and BIOS 6220. This course is an introduction to desktop mapping using ESRI's (Environmental Systems Research Institute) ArcView 10.X. The course covers geographic concepts as they apply to geographic information systems (GIS) and provides a basic understanding of mapping applications as a research and data evaluation tool in a public health environment. The student will develop a public health GIS project that requires the synthesis of skills and application of ArcView. see learning objectives
BIOS 7060 REGRESSION ANALYSIS (3)
Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Intermediate course in data analysis. Topics include multiple linear regression, dummy variables in regression, analysis of residuals, comparison of slopes, analysis of covariance, data transformations, and one- and two-way ANOVA. Use of SAS or SPSS. see learning objectives
BIOS 7080 DESIGN OF EXPERIMENTS (3)
Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Topics include simple and multiple linear regression, matrix notation, analysis of variance and quadratic forms, variable selection, polynomial regression, class (dummy variables, Analysis of Covariance, and regression diagnosis. SAS or SPSS required. see learning objectives
BIOS 7150 CATEGORICAL DATA ANALYSIS (3)
Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Techniques appropriate for analysis of categorical data including chi-square statistics, tests for fourfold tables, and logistic regression. Use of statistical package.see learning objectives
BIOS 7220 NONPARAMETRIC STATISTICS (3)
Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Introduces statistical techniques that can be applied to samples that come from populations having a wide class of distributions. Comparisons with parametric tests will be made. Topics include methods for single samples, paired and independent samples, and regression. Use of statistical package. see learning objectives
BIOS 7250 PRINCIPLES OF SAMPLING (3)
Prerequisite: BIOS 6030. Introduction to statistical sampling with emphasis on sample selection, methods of estimation, and techniques for calculating standard errors. Topics include: simple random sampling; stratified random sampling; systematic sampling; one, two, and multistage cluster sampling; and probability proportionate to size sampling. Use of statistical packages. see learning objectives
BIOS 7300 STATISTICAL METHODS FOR SURVIVAL DATA ANALYSIS (3)
Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Topics include analysis of survivorship data including estimation and comparison of survival curves, regression methods in the analysis of prognostic and etiologic factors, concepts of competing risks, and the analysis of clinical trial data. Software used for problem solving. Emphasis placed on the application of methods to the analysis of public health data with examples of clinical trials, cancer survivorship, contraceptive continuation rates, and other data sets for which there is partial follow-up of subjects. Use of SPSS or SAS. see learning objectives
BIOS 7400 CLINICAL TRIALS (3)
Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Topics include preparation of protocols, definition of objectives, population, treatment and endpoints, selection of appropriate study designs, computation of sample size and power, design of randomization procedures, interim data analysis methodology, ethical issues, early termination of trials and analysis of data arising in such trials. Content areas include cancer, cardiovascular, and pediatric diseases. Use of SPSS or SAS. see learning objectives
BIOS 8090 ADVANCED DESIGN OF EXPERIMENTS (3)
Prerequisites: BIOS 6030, BIOS 6040, BIOS 7080, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. More advanced topics than BIOS 7080, such as statistical analyses and constructions of balanced and partial balanced incomplete block designs, Latin square designs, Youden designs, fractional factorial and main effect designs, weighing designs, bioassay and surface response designs, and designs for diallel crossing. see learning objectives
BIOS 8160 ADVANCED CATEGORICAL DATA ANALYSIS (3)
Prerequisites: BIOS 6030, BIOS 6040, BIOS 7150, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. This course is a sequel to BIOS 7150 Categorical Data Analysis. Topics covered include polytomous outcome variables, continuous predictors, multiple outcome variables, panel data, impact of study design, and latent class analysis. see learning objectives
BIOS 8200 CAUSAL INFERENCE FOR BIOMEDICAL INFORMATICS
Prerequisite: BIOS 6030, BIOS 6040, BIOS 7060, and at least one of BIOS 6230, BIOS 6240, BIOS 6280, or instructor's approval. This course covers basic concepts and selected state-of-the-art statistical methods and theory of causal inference for biomedical informatics. It will empower students to draw causal conclusions and estimate direct causal effects from observational and experimental studies. Topics include: basic concepts in targeted statistical learning; structural causal models; v-fold cross-validation based super learner selection algorithm; targeted maximum likelihood estimators (TMLEs); comparisons between TMLEs and other estimators. see learning objectives
BIOS 8350 CLUSTERED AND LONGITUDINAL DATA ANALYSIS (3)
Prerequisites: BIOS 6030, BIOS 6040, at least one of BIOS 7060 or BIOS 8600, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. This course presents two of the major approaches to analysis of clustered and longitudinal data: marginal methods using generalized estimating equations and hierarchical (random effects) models. Techniques are applied when individuals are followed over time and when individuals are clustered within larger units. Techniques are applied to continuous, binary, and count outcomes. SAS and STATA are used to conduct the data analysis. see learning objectives
BIOS 8420 PRINCIPLES OF MEASUREMENT (3)
Prerequisites: BIOS 6030, BIOS 6040, BIOS 7060, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Covers assessment of reliability and validity, development of scales using methods such as paired comparisons and summated ratings, use of factor analysis and multiple dimensional scaling in scale development, and assessment of published scales in the health fields. see learning objectives
BIOS 8500 MONTE CARLO AND BOOTSTRAPPING METHODS (3)
Prerequisites: BIOS 6030, BIOS 6040; at least one of BIOS 6230, BIOS 6240, BIOS 6280; and at least one of the following: BIOS 7060, BIOS 7080, BIOS 7150, BIOS 7220, BIOS 7250, BIOS 7300, BIOS 7400. Methods used for Monte Carlo simulations and bootstrapping. Topics include how to design, program, and interpret a simulation study, Monte Carlo Markov Chain methods, Bayesian inference in Monte Carlo methods, uses of bootstrapping for estimation, jackknifing, and other resampling methods. see learning objectives
BIOS 8600 ADVANCED EVALUATION RESEARCH (3)
Prerequisites: BIOS 6030, BIOS 6040, BIOS 7060, and at least one of BIOS 6230, BIOS 6240, BIOS 6280. Designed for the advanced graduate student who is pursuing a career track in applied research. Topics covered include the specification of analytic models and research designs as well as the statistical techniques required to analyze quasi-experimental and observational data. Validity, research design, aggregation bias, model specification, analysis of covariance, time series analysis, and path analysis techniques will be covered. see learning objectives
BIOS 8800 APPLIED DATA ANALYSIS (3)
Prerequisites: BIOS 6030, BIOS 6040; at least one of BIOS 6230, BIOS 6240, BIOS 6280; and at least one of the following: BIOS 7060, BIOS 7080, BIOS 7150, BIOS 7220, BIOS 7250, BIOS 7300, BIOS 7400. This course introduces students to hands-on analysis and management. Students use real data to investigate how to formulate testable hypotheses, investigate and clean data, accommodate missing data, design and perform appropriate analyses, and keep written records of their analyses. Students also learn how to interpret and report the results of statistical analyses. Use of a statistical software package, preferably SAS, required. see learning objectives
BIOS 8820 MULTIVARIATE METHODS (3)
Prerequisites: BIOS 6030, BIOS 6040; either BIOS 7060 or BIOS 7080; and at least one of BIOS 6230, BIOS 6240, BIOS 6280. This is an applied course in multivariate analysis using the statistical software packages SAS and/or SPSS. Topics covered include: Hotelling T, multivariate regression and analysis of variance, profile analysis, discriminate analysis, principal components, factor analysis, and linear structural modeling. see learning objectives
BINF 6010 PRINCIPLES OF BIOINFORMATICS (3)
Prerequisites: None. This course deals with bioinformatics methods and tools used for analysis of genomics and proteomics data. The emphasis will be on the rationale as well as limitations of statistical methods and tools used in the field of bioinformatics. The course includes use of appropriate terminology, glossary and notations used in the field of genomics and proteomics; biological data storage and retrieval processes; methods used to decrypt information encoded in genomes; and design and analysis of bioinformatics data. see learning objectives
BINF 6100 Gene Molecular Biology (3)
Prerequisites: None. This is a comprehensive course that focuses on molecular biology of gene structure and gene function. The major contents cover (i) Gene structures (nucleic acids and protein-coding genes); (ii) Genetic information transfer (encoding, decoding, RNA processing and protein synthesis); (iii) Regulation of gene expression (at transcriptional, posttranscriptional and posttranslational levels); and (ix) Strategies for studying gene function (gene manipulation and cloning, gain- and loss-of-function). As the major goal of this course, upon the completion of the course, students are expected to get familiar with the principles of gene molecular biology as well as advanced biological techniques and methodologies for studies of gene function. see learning objectives
BINF 6200: BIOINFORMATICS COMPUTATIONS USING R (3)
Prerequisties: None. The emphasis of this course is the management and analysis of bioinformatics data at the molecular-level using the open source statistical computing environment R. The course will include applications of Bioconductor project for biological data, debugging programs, data manipulation, data description, and basic statistical analysis for bioinformatics data. see learning objectives
BINF 7010 POPULATION AND QUANTITATIVE GENETICS (3)
Prerequisite: BIOS 6040 or equivalent. A major goal of this course is to acquaint students with basic models of population and quantitative genetics and with empirical tests of these models. We will discuss the primary forces and processes involved in shaping genetic variation in natural populations (mutation, drift, selection, migration, recombination, mating patterns, population size and population subdivision), methods of measuring genetic variation in nature, and experimental tests of important ideas in population and quantitative genetics. see learning objectives
BINF 7160: ANALYSIS OF GENE EXPRESSION MICROARRAY DATA (3)
Prerequisite: BINF 6010. This course deals with statistical methodologies used in analyzing gene expression microarray data. Visualization techniques, data pre-processing and normalization methods, analysis of variance procedure and cluster analysis will be discussed. The course will also focus on methods used for the selection of differentially regulated genes. see learning objectives
BINF 7210: STATISTICAL METHODS IN BIOINFORMATICS (3)
Prerequisite: BINF 6010. The objective of this course is the development and application of statistical methods used in bioinformatics. The topics will include analysis of one DNA Sequence, modeling DNA, shotgun sequencing modeling, analysis of patterns (Overlaps counted and not counted), profiles and multiple alignments. see learning objectives
BINF 7300 BIOINFORMATICS APPROACHES TO GENOMICS (3)
This course is a comprehensive overview and dissection of genomics from the bioinformatics perspective. It focuses on the introduction of key genomics and bioinformatics concepts and current bioinformatic analyses strategies in the field of genomics. It also involves hands-on usage and application of various bioinformatics tools and software. The major contents include access to and analysis of genomics data using a variety of online databases and software packages, bioinformatic analyses of the genomic data at the DNA, RNA, protein levels, bioinformatic analysis of molecular evolution from genomics perspective, comparative genomics, intermolecular interactions and biological pathways.
BINF 7500 Epigenetics and Epigenomics (3)
Prerequisites: BIOS 6030 or equivalent, and BINF 6100 or TRMD 6780 or background in molecular biology/genetics, or genetic epidemiology. This course provides an overview of epigenetics and epigenomics. The course lectures will address the principles of epigenetic regulation of gene transcription, epigenome-environment interactions, and roles of epigenetic and epigenomic mechanisms in disease etiology. Current and emerging techniques and methodologies for epigenetic and epigenomic research will also be introduced. see learning objectives
BINF 7600: Nutritional genomics for disease prevention and intervention (3)
Prerequisite: Completion or concurrent enrollment in BIOS 6030 (introductory biostatistics) or equivalent, and BINF 6100 (gene molecular biology) or background in molecular biology/genetics, genetic epidemiology, or equivalent or instructor's approval. The course is a comprehensive overview of nutrigenetics/nutrigenomics and their application for disease prevention and intervention. The lecture will address how the nutrigenomics and nutrigenetics knowledge may potentially lead to personalized diet to prevent and improve nutritionally related diseases, such as osteoporosis, cancer, obesity, type 2 diabetes, cardiovascular disease, and inflammation disease. Current and emerging tools for nutrigenetics/nutrigenomics research will also be introduced. see learning objectives
BINF 8100 Statistics in Human Genetics (3)
Prerequisites: BIOS 6040 or equivalent, and one of BINF 7010, BIOS 7060, BIOS 7080 or equivalent or instructor's approval. This course is concerned with the statistical analysis of human genetics data. The topics include analytical methods for genetic markers such as single nucleotide polymorphisms, next generation sequence data, statistical analyses for disease susceptibility gene mapping such as linkage analysis and association study, and other topics such as methods in population structure detection. see learning objectives
BIOS 7990 INDEPENDENT STUDY (1-6)
BIOS 9970 DISSERTATION (0)
BIOS 9980 MASTERS THESIS (0-6)
BIOS 9990 DISSERTATION RESEARCH (2)
