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GBDS Courses

View current course offerings at the Office of the Registrar.

BIOS 6030 INTRODUCTORY BIOSTATISTICS (3) This is a beginning course in applied biostatistics. The objective of the course is to introduce the student to biostatistical methods and to understand the underlying principles, as well as practical guidelines of “how to do it” and “how to interpret it” as the role they can play in decision making for public health majors. The course covers both graphical and numerical methods of describing data sets, an introduction to probability and probability distributions, estimation, hypothesis testing, power and sample size estimations. Faculty: S. Srivastav, A. Shankar, J. Lefante, Y. Liu. Offered: Every Semester. Prerequisite(s): None.

BIOS 6040 INTERMEDIATE BIOSTATISTICS (3) This is an intermediate course in applied biostatistics. The course covers Analysis of Variance and Multiple Regression and Correlation Analysis, and Logistic Regression. The focus will be on numerical computation and interpretation of results of statistical application using statistical packages. Faculty:  A. Shankar, H. Qin, W. Tang, J. Lefante. Offered: Fall and Spring. Prerequisite(s): BIOS 6030 and instructor approval based on prior knowledge of statistical package.

BIOS 6220 DATABASE MANAGEMENT (3) An introduction to the principles and application of data management, techniques in data collection, data cleaning, data reporting, database design, and implementing databases for managing large data systems. After taking the course, students will be able to create databases with applications to public health intervention and surveillance, use SQL to administrate, mage, and retrieve data for statistical analysis. Faculty: J. Zhao, J. Shaffer. Offered: Spring. Prerequisite(s): Basic knowledge of MS Office.

BIOS 6230 COMPUTER PACKAGES SAS (1) Introduces SAS (Statistical Analysis System) to students who are new to the program. Students will develop essential SAS programming skills for analysis of medical and health-related data. Faculty: T. Niu. Offered: Every Semester. Prerequisite(s): BIOS 6030.

BIOS 6240 COMPUTER PACKAGE FOR SPSS (1) Introduction to SPSS (Statistical Package for the Social Science) for Windows for data management and analysis. Provides a foundation for the use of the SPSS software through example programs that analyze health-related data sets. Students will develop technical skills necessary for analysis of health-related data sets. Faculty: T. Niu. Offered: Every Semester. Prerequisite(s): BIOS 6030.

BIOS 6270 Introduction to R (1) Introduces the R computer package for students who are new to statistical programming in R. Topics include creating, importing, and updating data files, managing, restructuring and exporting data, constructing graphs by applying plotting functions, and performing basic statistical analysis using R. Students will develop critical analytic capabilities in performing various statistical tasks, e.g., descriptive and exploratory data analysis, conducting analysis of variance (AVA), and fitting linear models. Faculty: T. Niu. Offered: Every Semester. Prerequisite(s): BIOS 6030 concurrent.

BIOS 6280 INTRODUCTION TO STATA (1) Introduces STATA to students who are new to the program.  Uses STATA to mage data by creating, updating and restructuring data files. Students will be able to use STATA to accomplish a variety of statistical tasks such as computing summary statistics, creating visual displays of data, performing a variety of statistical tests including Student’s t test and analysis of variance (AVA), and constructing a linear regression model. Faculty: T. Niu. Offered: Every Semester. Prerequisite(s): BIOS 6030 concurrent.

BIOS 6300 INTRODUCTION TO ArcGIS (1) This course covers the elementary concepts and applications for mapping using the ArcGIS software.  The course focuses on a wide variety of public health applications and is applicable to virtually all academic and professional settings where mapping is used.  Each lecture begins with a PowerPoint presentation to introduce fundamental mapping concepts and is followed with in-class exercises to reinforce hands-on application.  Two in-class, paper-based exams are given to monitor and assess students’ understanding of the course concepts. Faculty: T. Niu, J. Shaffer. Offered: Every Semester. Prerequisite(s): BIOS 6030 concurrent.

BIOS 6310 INTRODUCTION TO PUBLIC HEALTH INFORMATICS (3) Public health informatics is a scientific discipline that applies information and computer sciences and technologies to every field of public health to improve population health. In this course, students will learn about the foundation and principles of public health informatics, and explore how information and computer sciences, including databases, networks, information systems, technologies and computer applications, can be applied to enhance public health practice, research and education. It will look at the entire process, from systems conceptualization and design to project planning and development, to system implementation and use. The course will also cover the issues about management, privacy and confidentiality in development and utilization of information systems.  Importantly, students will gain hands-on experience in exploring some key public health informatics applications or public health information systems that currently serve as major sources of data and information. This course is one of the two public health informatics courses, 1) Introduction to Public Health Informatics and 2) Advanced Public Health Informatics. The course, Introduction to Public Health Informatics, that introduces an overview and principles of public health informatics will serve as a key foundation for students to pursue the course Advanced Public Health Informatics. Advanced Public Health Informatics will cover new challenges facing the public heath informatics systems, and case studies for applications of information systems development.  Faculty: A. McCoy. Offered: Fall (Online). Prerequisite(s): Students are expected to have basic computer knowledge.

BIOS 6350 ENVIRONMENTAL BIOSTATISTICS (3) The objective of this course is the application of statistical methods to the collection and analysis of environmental data. The focus of the course will be on field sampling designs for air, soil and water samples, statistical techniques relating to environmental data and developing predictive models for environmental data. Faculty: A. Shankar. Offered: Fall. Prerequisite(s): BIOS 6030.

BIOS 6800 PUBLIC HEALTH GIS (3) The course is an introduction to desktop mapping and spatial analysis. The first part of the course covers geographic information systems (GIS) concepts and mapping using the ArcGIS software. The second part of the course covers introductory spatial analytical techniques, including spatial autocorrelation quantification, cluster analysis, and spatial modeling. The student will develop a public health GIS project that requires the synthesis of mapping and spatial analysis. Faculty: J. Shaffer. Offered: Spring. Prerequisite(s): BIOS 6220.

BIOS 7060 REGRESSION ANALYSIS (3) An advanced course in applied biostatistics. Provides selected statistical techniques for analyzing data on multiple variables, both continuous and categorical. Presents methods for model specification, estimation, inference and prediction. Conducts model evaluation/diagnostics and assumption validation, such as normality, heteroscedasticity, outliers and influential point identification, autocorrection, and multicollinearity. Applies appropriate remedial measures such as transformation, polynomial models, and weighted least squares. Compares non-linear regression /other regression techniques with linear regressions. This course provides the student with insight into the application of regression techniques to the medical and health sciences. It will focus on statistical methodology with emphasis on selection of appropriate applications and interpretation of results. Faculty: J. Li. Offered: Spring. Prerequisite(s): BIOS 6030 & 6040; and one of BIOS 6230, 6240, 6270, 6280.

BIOS 7080 DESIGN OF EXPERIMENTS (3) This course deals with fundamental topics in design of experiments including principle theory of experimental designs (randomization, replication, and balance). It focuses the main elements of statistical thinking in the context of experimental design such as completely randomized design, randomized complete block design, experiments with two factors, factorial design, nested designs, repeated measurement design, and split-plot designs. Faculty: S. Srivastav. Offered: Spring. Prerequisite(s): BIOS 6030 & 6040; and one of BIOS 6230, 6240, 6270, 6280.

BIOS 7150 CATEGORICAL DATA ANALYSIS (3) Fundamental concepts and methods for analysis of categorical outcomes.  Topics include analysis of 2-way tables, unconditional and conditional logistic regression, power and sample size computation, and modeling of dependent categorical outcomes via mixed models and GEE methods.  Course covers the mathematical basis of the statistical procedures but the emphasis is on application of the methods using statistical software and interpretation of results. Faculty: L. Myers. Offered: Fall. Prerequisite(s): BIOS 6030 & 6040; and one of BIOS 6230, 6240, 6270, 6280.

BIOS 7220 NONPARAMETRIC STATISTICS (3) Nonparametric inferential statistical methods are introduced. Topics include single, paired, independent, and multiple sample hypothesis testing and confidence interval methods; n parametric regression and correlation methods; categorical data and measures of concordance. Faculty: L. Myers, J. Lefante, S. Srivastav. Offered: Spring. Prerequisite(s): BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, or BIOS 6280.

BIOS 7250 PRINCIPLES OF SAMPLING (3) This course introduces core principles of survey sampling, with emphasis on sampling plans, methods of estimating unknown parameters of population and subdomain, and techniques for calculating precisions of the estimators. Topics include: basic concepts in survey sampling, simple random sampling; stratified random sampling; systematic sampling; one-, two- and multi-stage cluster sampling; probability appropriate to size sampling. Faculty: H. Qin, J. Lefante. Offered: Spring. Prerequisite(s): BIOS 6030; and one of BIOS 6230, 6240, 6270, 6280.

BIOS 7300 SURVIVAL DATA ANALYSIS (3) 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, and other data sets for which there is partial follow-up of subjects. Faculty: W. Tang, J. Lefante. Offered: Fall. Prerequisite(s): BIOS 6030 & 6040, and one of BIOS 6230, 6240, 6270, 6280.

BIOS 7380 BAYESIAN INFERENCE (3) This course provides an introduction to Bayesian theory and methods. Specifically, students will learn fundamentals and applications of Bayes' theorem, likelihood principle, conjugate prior distributions for common statistical models, and Markov chain Monte Carlo techniques for approximating posterior distributions. Applications of Bayesian inference to solving practical problems are illustrated. Faculty: T. Niu. Offered: Spring '17 (Initial Course Offering) Prerequisite(s): BIOS 6030 & 6040; and one of BIOS 6230, 6240, 6270, 6280.

BIOS 7400 CLINICAL TRIALS (3) Covers design, implementation, analysis and reporting of clinical trials. Topics encompass trial design, hypothesis formulation and testing, methods of randomization, ethics, sequential trials, sample size determination, blinding, subject recruitment, data collection and management, quality control, monitoring outcomes and adverse events, interim analysis, statistical methods in analyzing trial data, and addressing scientific issues in reporting and interpreting trial results. Faculty: T. Niu. Offered: Every other Fall. Prerequisite(s): BIOS 6030 & 6040; and one of BIOS 6230, 6240, 6270, 6280. Concur: BIOS 6030, BIOS 6230, BIOS 6240.

BIOS 7650: STATISTICAL LEARNING IN DATA SCIENCE (3) This course provides detailed overviews over the evaluation and application of statistical learning theories and techniques for inference and prediction in data science, particular for biological and public health data. Topics include linear and nonlinear models, resampling techniques, tree-based methods, unsupervised learning such as clustering, support vector machine, graphical models, etc. Working on real and/or simulated data through assignments, students will apply the knowledge learned and practice their skills in solving various biological and public health problems, such as sequence alignment, gene prediction, subtype identification and classification, and disease risk and prognosis prediction. Discussion on model assessment and selection are also included. Faculty: Jian Li. Offered: Fall. Prerequisite(s): BIOS 6030, one of BIOS 6230/6240/6280, BIOS 6040; or instructor approval

BIOS 8000 DOCTORAL STUDENT JOURNAL CLUB (0) This course is intended to improve students' ability in interpreting, evaluating, critiquing, presenting, and communicating the elements, concepts, findings, and implications from current Biostatistics and Bioinformatics research literatures in a seminar setting.  All enrolled students will be expected to give at least one oral presentation and participate in the student-led discussions.  Feedback to each presenter will be given orally, in writing and/or through e-mails by faculty and peer students.  At the end of the course, students will gain experience in assessing the value of research findings from selected publications to biostatistics and bioinformatics research. Faculty: H. Deng. Offered: Spring. Prerequisite(s): Enrollment in Doctoral Program or Instructor Approval.

BIOS 8200 CAUSAL INFERENCE FOR BIOMEDICAL INFORMATICS (3) This course presents state of the art statistical methods and theory of causal inference for biomedical informatics. It will empower students to draw causal conclusions from observational and experimental studies and establish their theoretical properties. Topics include: structural causal models and causal graphs; causal target parameter, interventions and counter factuals; cross validation based super machine learning; targeted maximum likelihood estimators (TMLEs); comparisons between TMLEs and other estimators; estimation for causal direct effect; diagnosing and rectifying bias due to positivity violations. Faculty: H. Qin. Offered: Every other Spring. Prerequisite(s): BIOS 6030 & 6040; and one of BIOS 6230, 6240, 6270, 6280; BIOS 7060, MATH 6080.

BIOS 8350 CLUSTERED AND LONGITUDINAL DATA ANALYSIS (3) This course presents two of the major approaches to analysis of clustered and longitudinal data: marginal methods using generalized estimating equations and hierarchal (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. Faculty: W. Tang. Offered: Every other Spring. Prerequisite(s): BIOS 6030, BIOS 6040, BIOS 7060, and at least one of BIOS 6230, 6240, 6280, or instructor approval.

BIOS 8500 MONTE CARLO AND BOOTSTRAPPING METHODS (3) This course introduces students to hands-on data analysis and management. Students also 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 documentation of their analyses. Students also learn how to interpret and report the results of statistical analyses, both orally and in writing. Use of a statistical software package, preferable SAS, required. Faculty: L. Myers. Offered: Every other Spring. Prerequisite(s): At least 2 of 5 selected BIOS 7000 level courses:  7060, 7080, 7150, 7220, 7300.

BIOS 8800 APPLIED DATA ANALYSIS (3) This course introduces students to hands-on data analysis and management. Students use real data to investigate how to formulate testable hypotheses, investigate and clean data, design and perform appropriate analyses, and keep written documentation of their analyses. Students also learn how to interpret and report the results of statistical analyses, both orally and in writing. Use of statistical software, preferable SAS, is required. Faculty: L. Myers. Offered: Every other Fall. Prerequisite(s): At least 2 of 5 selected BIOS 7000 level courses:  7060, 7080, 7150, 7220, 7300.

BIOS 8820 MULTIVARIATE METHODS (3) This is a doctoral level course that covers techniques used to conduct analysis with more than one outcome variable. The focus will be on association methods and predictive models between multiple independent and multiple dependent variables. Additionally the students will learn techniques for variable reduction, path models, and factor analysis. Students will conduct numerical computation and interpretation of results of statistical application using statistical packages. Faculty: A. Shankar. Offered: Every other Fall. Prerequisite(s): Doctoral status required. Students should have completed at least two 7000 level biostatistics courses and have working knowledge of programmable statistical software, (SAS, R, STATA).

BINF 6010 PRINCIPLES OF BIOINFORMATICS (3) This course introduces basic bioinformatics algorithms used in analysis of biological sequence data. These algorithms represent the theoretical foundations for the latest bioinformatics analytical methods, including next generation sequencing data analyses. Faculty: Y. Liu. Offered: Fall. Prerequisite(s): None.

BINF 7300 BIOINFORMATICS APPROACHES TO TRANSCRIPTOMICS (3) Transcriptomics has evolved to a major area of research and application of bioinformatics. It is a dynamic area where new technologies and novel statistical and bioinformatics approaches sprout every day. This course is a comprehensive and in-depth overview and discussion on a wide spectrum of bioinformatics and statistical topics related to transcriptomics. The major focus is on various stages and aspects of bioinformatics and statistical analyses of transcriptomics data. Faculty: Y. Liu. Offered: Spring. Prerequisite(s): BIOS 6030.

BINF 7500 EPIGENETICS AND EPIGENOMICS (3) Provides a comprehensive, state-of-the-art introduction to basic and advanced knowledge of epigenetics and epigenomics. Reviews the principles and recent progresses in epigenetic regulation of gene transcription, epigenome-environment interactions, and roles of epigenetic and epigenomic mechanisms in disease etiology. Introduces current and emerging techniques and methodologies for assessing epigenetic features. Presents and discusses recent cutting-edge epigenetic and epigenomic studies. Faculty: H. Shen. Offered: Fall. Prerequisite(s): TRMD 6780 or background in molecular biology, molecular genetics or genetic epidemiology.