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BIOS 6030 INTRODUCTORY BIOSTATISTICS (3)
Fall/ Spr/ Sum (Period I). Prerequisites: None.  Course Description: This is a beginning course in applied biostatistics. 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 estimation.  The objective of this course is to introduce the students 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 will focus on both descriptive and inferential statistical techniques, with emphasis on selection of appropriate application and interpretation of results. Faculty: J. Lefante, J. Shaffer, A. Shankar, S. Srivastav, Yao-Zhong Liu, H. Shen.  see learning objectives

BIOS 6040 INTERMEDIATE BIOSTATISTICS (3)
Fall/ Spr. Prerequisites: BIOS 6030 and either BIOS 6230 ; BIOS 6240; or BIOS 6280.  Course Description: This is an intermediate course in applied biostatistics.  The course covers Analysis of Variance and Multiple Regression and Correlation Analysis, Analysis of Covariance and Logistic Regression.  The focus will be on numerical computation and interpretation of results of statistical application using SAS and SPSS. Faculty:  J. Lefante, A. Shankar, H. Qin.  see learning objectives

BIOS 6220 DATABASE MANAGEMENT (3)
Spr. Prerequisites: None. Course Description: 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 2010 on microcomputers using health related data systems. Faculty: L. Zhao, J. Shaffer. see learning objectives

BIOS 6230 COMPUTER PACKAGE SAS (1)
Fall/ Spr/ Sum (Period II). Prerequisties: None.  Course Description: An introduction to statistical  programming using the Statistical Analysis System (SAS) software.  Strongly recommended for all students without prior statistical programming experience who plan to complete advanced statistics coursework.  Faculty: L. Myers.  see learning objectives

BIOS 6240 COMPUTER PACKAGE FOR THE STATISTICAL SCIENCES SPSS (1)
Fall/ Spr/ Sum. Prerequisites: BIOS 6030 (can be taken concurrently). Course Description: An introduction to computing using the SPSS statistical package. Strongly recommended for all students without prior computing experience who take advanced statistics and computing courses. Faculty: L. Myers.  see learning objectives

BIOS 6280 INTRODUCTION TO STATA (1)
Fall/ Spr/ Sum. Prerequisites: BIOS 6030 recommended (can take concurrently).  Course Description: The objective of this course is to introduce STATA version 9 for students who are new to the program.  The focus will be to manage data using STATA as creating, updating and restructuring data files.  Students will be able to use STATA to accomplish a variety of statistical tasks as summary statistics, ANOVA, regression and logistic regression.  Faculty: L. Myers.  see learning objectives

BIOS 6300 INTRODUCTION TO ArcGIS (1)
Fall/ Spr. Prerequisites: None.  Course Description: This course provides the foundation for becoming a successful ArcView®, ArcEditorTM, or ArcInfoTM 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 ArcMapTM, ArcCatalogTM, and ArcToolboxTM and explore how these applications work together to provide a complete GIS software solution.  Faculty: J. Shaffer.  see learning objectives

BIOS 6350 ENVIROMENTAL BIOSTATISTICS (3)
Fall. Prerequisites: BIOS 6030.  Course Description: 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.  Faculty: A. Shankar.  see learning objectives

BIOS 6800 INTRODUCTION TO PUBLIC HEALTH GIS (3)
Spr. Prerequisites: BIOS 6030 and BIOS 6220.  Course Description: This course is an introduction to desktop mapping using the ESRI's (Environmental Systems Research Institute) ArView 10.X. The course covers geographical concepts as they apply to GIS (Geographical Information Systems) and provides a basic understanding of mapping applications as a research and data evaluation tool in a public health environment. Covers elements of disease mapping. The Student will develop a public health GIS project that requires the synthesis of skills and application of ArcView.  Faculty: J. Shaffer.  see learning objectives

BIOS 7060 REGRESSION ANALYSIS (3)
Spr. Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, or BIOS 6280.  Course Description: This is an advanced course in applied biostatistics. The course covers selected statistical techniques for analyzing data on multiple variables . Examples from medical and health related fields will be used. 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.  Faculty: J. Li, T. Niu.  see learning objectives

BIOS 7080 DESIGN OF EXPERIMENTS (3)
Spr. Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, or BIOS 6280.  Course Description: This course deals with basic techniques and methodology for design of experiments including principle theory of experimental designs (randomization, replication and balance). It introduces the main elements of statistical thinking in the context of most commonly used experimental designs such as completely randomized design, randomized complete block design, experiments with two factors, repeat measurements design, factorial design, and nested designs.  Faculty: S. Srivastav.  see learning objectives

BIOS 7150 CATEGORICAL DATA ANALYSIS (3)
Fall. Prerequisites: BIOS 6030, BIOS 6040, and working knowledge of statistical software. Course Description: Statistical methods for the analysis of categorical data, including statistics for 2x2 contingency Tables, chi-squared statistics for 2-way contingency tables, and logistic regression. Topics include power, model selection, model interpretation and odds ratios, matched designs, and repeated measures regression method. Faculty: L. Myers. see learning objectives

BIOS 7220 NONPARAMETRIC STATISTICS (3)
Spr. Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, BIOS 6280.  Course Description: Introduces statistical techniques that can be applied to samples that come from populations having any of a wide class of distributions. Comparisons with parametric tests will be made. Topics include methods for single, paired, and independent samples and regression. Use of statistical package.  Faculty: J. Lefante, L. Myers, S. Srivastav.  see learning objectives

BIOS 7250 PRINCIPLES OF SAMPLING (3)
Spr. Prerequisite: BIOS 6030.  Course Description: 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; probability proportionate to size sampling. Use of statistical packages.  Faculty: J. Lefante.  see learning objectives

BIOS 7300 STATISTICAL METHODS FOR SURVIVAL DATA ANALYSIS (3)
Every other Fall. Prerequisites: BIOS 6030, BIOS 6040, and at least one of BIOS 6230, BIOS 6240, or BIOS 6280. Course Description: Topics Include analysis of  survivorship data including estimation and comparison of survival curves, regression methods in the analysis of prognostic and etiological 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.  Faculty: J. Lefante.  see learning objectives

BIOS 7400 CLINICAL TRIALS (3)
Every other Fall. Prerequisites: BIOS 6030, BIOS 6230, BIOS 6240 or equivalent.  Course Description: Topics include preparation of protocols, definition of objectives, population, treatment and endpoints, selection of appropriate study designs, computation of sample size, power, design of randomization procedures, interim data analysis methodology, ethical issues, early termination of trial, analysis of data arising in such trials, particularly Cox regression and other survival analysis programs. Content equivalent; BIOS 6230, BIOS 6240 or equivalent.  Faculty: T. Niu, S. Srivastav.  see learning objectives

BIOS 8000 DOCTORAL STUDENT JOURNAL CLUB (0)
Fall/ Spr. Prerequisites: Enrollment in Doctoral Program/Instructor's approval.  Course Description: 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.  Feedbacks 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, J. Li.  see learning objectives

BIOS 8090 ADVANCED DESIGN OF EXPERIMENTS (3) :: {Offered upon demand}
Spr. Prerequisites: BIOS 6030, BIOS 6040, BIOS 7080.  Course Description: Knowledge of Statistical Software (SAS, SPSS, or R).  This is an advanced course of BIOS 7080 Design of Experiments. This course deals with more advanced topics in design of experiments including principle of experimental designs (randomization, replication and balance), balanced and partially balanced incomplete block design, Latin square design, confounding, partial confounding, fractional factorial design, weighing designs, bio-assays and diallel crosses designs. The optimality and constructions of designs, and recent development are also discussed.  Faculty: S. Srivastav.  see learning objectives

BIOS 8200 CAUSAL INFERENCE FOR BIOMEDICAL INFORMATICS
Spr. Prerequisite: BIOS 6030, BIOS 6040, BIOS 7060, MATH 6080 and at least one of BIOS 6230, BIOS 6240, or BIOS 6280.  Course Description: 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 counterfactuals; 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.  see learning objectives

BIOS 8350 CLUSTERED AND LONGITUDINAL DATA ANALYSIS (3)
Spr. Prerequisites: BIOS 6030, BIOS 6040, BIOS 7060, and working knowledge of statistical software.  Course Description: 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.  Faculty: L. Myers, J. Lefante.  see learning objectives

BIOS 8500 MONTE CARLO AND BOOTSTRAPPING METHODS (3)
Every other Year (Alternating Fall or Spr). Prerequisites: At least one of BIOS 7000-level courses (BIOS 7060, 7080, 7150, 7220, 7300).  Course Description: This course introduces the methods used for Monte Carlo simulations and bootstrapping. Topics include how to design, program, and interpret a simulation study, uses of bootstrapping for estimation, jackknifing, and other resampling methods. Monte Carlo Markov Chain methods and Bayesian inference in Monte Carlo methods will be briefly introduced. Use of a computer programming language, basic computer skills, and/or use of SAS required.  Faculty: L. Myers.  see learning objectives

BIOS 8800 APPLIED DATA ANALYSIS (3)
Every other Year (Alternating Fall or Spr). Prerequisites: At least one of BIOS 7000-level courses (BIOS 7060, 7080, 7150, 7220, 7300).  Course Description: 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, 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, preferably SAS, required.  Faculty: L. Myers.  see learning objectives

BIOS 8820 MULTIVARIATE METHODS (3)
Fall. Prerequisites: BIOS 6030, BIOS 6040, either BIOS 7060 or BIOS 7080; and at least one of BIOS 6230, BIOS 6240, or BIOS 6280.  Course Description: This course covers techniques used conduct analysis with more than one outcome variable. Classical techniques include Hotelling's T squared, multivariate analysis of variance, discriminant function analysis, canonical correlation, principle component analysis and descriptive factor analysis. The course also introduces structural equation modeling to conduct confirmatory factor analysis and causal modeling.  Faculty: A. Shankar.  see learning objectives

BINF 6010 PRINCIPLES OF BIOINFORMATICS (3) 
Spr. Prerequisites: None.  Course Description: This course deals with bioinformatics methods and tools used for analysis of genome and protein sequence data. The emphasis will be on principles, rationales, and applications of major bioinformatics methods, strategies and tools. The course also involves introduction of key concepts, terminology and notations in bioinformatics, biological data storage and retrieval processes, and biological database searching.  Faculty: Yao-Zhong Liu.  see learning objectives

BINF 6100 GENE MOLECULAR BIOLOGY (3)
Fall. Prerequisites: None.  Course Description: 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.  Faculty:  L. Zhao.  see learning objectives

BINF 7010 POPULATION AND QUANTITATIVE GENETICS (3) 
Spr. Prerequisite:  BIOS 6040 or equivalent.  Course Description: This course focuses on empirical and theoretical population and quantitative genetics. A major goal of this course is to make students familiar with the basic models of population and quantitative genetics and to acquaint students 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.  Faculty: J. Li.  see learning objectives

BINF 7210: STATISTICAL METHODS IN BIOINFORMATICS (3)
Fall. Prerequisite: BINF 6010, BINF 6200 and BINF 7160.  Course Description: 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.  Faculty: S. Srivastav.  see learning objectives

BINF 7300 BIOINFORMATICS APPROACHES TO TRANSCRIPTOMICS (3)
Spr. Prerequisites: BIOS 6030.  Course Description: Transcriptomics has evolved into 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 analysis of transcriptomics data.  Faculty: Yao-Zhong Liu.  see learning objectives


BINF 7500 EPIGENETICS AND EPIGENOMICS (3)

Fall. Prerequisites: BINF 6100 or TRMD 6780 or background in molecular biology, molecular genetics or genetic epidemiology.  Course Description: 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 mechanism in disease etiology. Current and emerging techniques and methodologies for epigenetic and epigenomic research will also be introduced.  Faculty: H. Shen.  see learning objectives

BINF 7600:  NUTRITIONAL GENOMICS (3)
Spr. Prerequisite: BIOS 6030 or equivalent, and BINF 6100 or background in molecular biology/genetics, genetic epidemiology, or equivalent or instructor's approval.  Course Description: The rapid development of bioinformatics technology brought us the age of nutrigenomics and nutrigenetics.  The ability of the future nutritionists and healthcare professionals to optimize diet recommendation requires an understanding how nutrients affect gene expression, and how genetic variants are associated with a dietary response.  The course is a comprehensive overview of nutrigenomics and nutrigenetics and their application for disease prevention and intervention.  Current and emerging tools for nutrigenetics/nutrigenomics research  will be introduced.  The lecture will also address how the nutrigenomics knowledge may potentially lead to personalized diet to prevent and improve nutritionally related disease, such as osteoporosis, cancer, obesity, type 2 diabetes, cardiovascular disease , and inflammation disease.  Faculty: L. Zhao.  see learning objectives

BINF 8100 STATISTICS IN HUMAN GENETICS (3)
Fall. Prerequisites: BIOS 6040 or equivalent or instructor's approval. (Can take concurrently at least one of BINF 7010, BIOS 7060, BIOS 7080 or equivalent or instructor's approval.)  Course Description: 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 analysis for disease susceptibility gene mapping such as linkage analysis and association study, and other topics such as methods in population structure detection.  Faculty: J. Li.  see learning objectives

SPHU 3160 BIOSTATISTICS IN PUBLIC HEALTH (3)
Fall/ Spr. Prerequisites: None.  Course Description: This course provides an overview of various statistical methods used in public health practice and research. Emphasis is on application of appropriate methods and interpretation of results. Examples and problems from public health settings will be included. Various statistical software will be used to analyze data (excel, SPSS and others), but prior computing experience is not required. Topics covered include methods of summarizing data and estimation and hypothesis testing techniques, including the t-test, the chi-square test, the analysis of variance, correlation analysis, and linear regression.  Faculty: J. Lefante, S. Srivastav.  see learning objectives

BIOS 7990 INDEPENDENT STUDY (1-6)

BIOS 9970 DISSERTATION (0)

BIOS 9980 MASTERS THESIS (0-3)

BIOS 9990 DISSERTATION RESEARCH (2)

 

New course offerings beginning in Fall 2014:

  • BIOS 6270 INTRODUCTION TO R
    Fall/ Spr (Period I). Prerequisites: BIOS 6030.  Course Description: The objective of this course is to introduce the R computer package to students who are new to statistical programming in R. The major 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, analysis of variance (ANOVA), and fitting linear models.  Faculty: T. Niu.  see learning objectives

  • BIOS 7390 META-ANALYSIS: METHODS AND APPLICATIONS
    Fall. Prerequisites: BIOS 6030 and BIOS 6040.  Course Description: This course provides a concrete introduction to statistics methods for meta-analysis and their applications in a broad spectrum of quantitative research areas. Students will obtain knowledge on essential steps in conducting a meta-analysis, particularly on conceptualizing a study hypothesis, performing a literature search, coding and extracting pertinent information from eligible studies, developing appropriate statistical models, testing for homogeneity, handling missing data, and applying a Bayesian meta-analysis. Students could accomplish an independent meta-analysis project or a specific meta-analysis research protocol, or complete a written critique of a published meta-analysis paper in their own research area.  Applications of Bayesian inference to solving practical problems are illustrated. Faculty: T. Niu.  see learning objectives

  • BINF 6300 INTRODUCTION TO PUBLIC HEALTH INFORMATICS :: New course flyer
    Spr. Prerequisites: Students are expected to have basic computer technologies or skills, such as MS excel and Internet exploring.  Course Description: 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.  This course will introduce students to an overview of public health informatics.  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 currently served 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 Pulbic 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 that will cover new challenges facing the emerging public health informatics systems and case studies for applications of information systems development. Faculty: A. McCoy.  see learning objectives

  • BINF 6400 DATABASES AND SQL
    Fall. Prerequisites: None.  Course Description: This course covers the concepts, principles, skills, and techniques of modern database management systems (DBMS).  Structured Query Language (SQL), the most commonly used language for database management, will be introduced.  Health-related data will be used as examples to explain the concepts of databases and SQL.  Topics include design, implementation, and management of database systems, focusing on database design and the SQL language.  Students will design databases and test their learned skills using MySQL and MS SQL Server DBMS.  After taking the course, students will have learned how to design, construct, and test an integrated database system for use in a commercial environment.  Faculty: L. Zhao.  see learning objectives

  • BINF 7310 ADVANCED PUBLIC HEALTH INFORMATICS
    Fall. Prerequisites: BINF 6300.  Course Description: This is the second part of the beginning course in Public Health Informatics (BINF 7300).  Based on the first part of the course that introduces some general principles of Public Health Informatics, this course covers new challenges facing the emerging public health informatics systems, including geographic information systems, expert systems for public health, and use of information  technology to promote delivery of preventive medicine in primary care.  The course also covers case studies for applications of information systems development to illustrate the meaning and importance of informatics and effective information systems  to modern public health practice.  Faculty: Yao-Zhong Liu.  see learning objectives


 
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Department of Biostatistics, 1440 Canal Street, Suite 2001, New Orleans, LA 70112, 504-988-5164 kbranley@tulane.edu