Department of Biostatistics and Bioinformatics  

Research opportunities currently available for Master's and Ph.D. students!



Biostatistics is a branch of applied statistics and is concerned with developing and using techniques to summarize and analyze medical and biological data.

The teaching and research programs prepare the student to be part of an interdisciplinary team for conducting medical and public health research. Students learn to extract and report data from existing databases, to create new databases as appropriate, and to analyze these data.  Advanced level students learn to address issues in the development and appropriate use of statistical methods. 


Lognormal Density


New course offerings beginning in Fall 2014:

    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

    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

    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

    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

    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

New course offering began Spring 2014. Will be taught again Spring 2015:

    Prerequisites: BIOS 6030 and BIOS 6040.  Course Description: Bayesian inference is a statistical inferential method based on Bayes' theorem.  Thanks to significant advances of computer software, making inference based on Bayesian methods has become increasingly popular, which provides a rigorous and flexible statistical framework to combine prior beliefs with new observations.  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.  see learning objectives
(click for full list of courses)

Mission Statement

The mission of the Department of Biostatistics is to advance the discipline by training students in methods research and its application, conducting methodological and collaborative interdisciplinary research in the fields of public health and medicine, and by providing to the academic, research and professional committees.




Department of Biostatistics, 1440 Canal Street, Suite 2001, New Orleans, LA 70112, 504-988-5164