Teaching

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Math 679

Statistical Bioinformatics

Objectives:
The purpose of this course is to introduce students to bioinformatics - an interdisciplinary area of study that combines techniques and knowledge in mathematical, statistical, computational, and life sciences to understand the biological significance of genetic sequence data. This type of high-dimensional and noisy data presents special challenges in data analysis, statistical modeling and interpretation of results. This course focuses on the role of statistics in bioinformatics. With hands on projects, students will learn to use statistical software and other Bioinformatics resources on the Internet. Lectures and class discussion will emphasize on the statistical models and methods underlying the algorithm design of these computational tools.

Pre-requisite: Math 575 or Instructor's permission
Credits: 3

Math 676

Computational Statistics

Syllabus:
Computational Statistics includes statistical visualization and other computationally-intensive methods of statistics. It is built on the statistical theory and methods, and includes visualization, statistical computing, and Monte Carlo methods.

This course is about modern, computationally-intensive methods in statistics. It emphasizes the role of computation as a fundamental tool of discovery in data analysis, of statistical inference, and for development of statistical theory and methods. We will use MATLAB, SPLUS and R as programming tools.

Following topics will be studied:
Monte Carlo studies in statistics, Computational inference, Data partitioning and resampling, Numerical methods in statistics ("statistical computing"), Nonparametric probability density estimation, Statistical models and data fitting

Math 675

BIOSTATISTICS

Syllabus:
General overview, descriptive statistics, probability, discrete probability distributions, continuous probability distributions, estimation, hypothesis testing: one-sample inference, hypothesis testing: two-sample inference, nonparametric methods, hypothesis testing: categorical data, regression and correlation methods, multisample inference, design and analysis techniques for epidemiologic studies, hypothesis testing: person-time data. Use of SPSS/SAS/S-Plus statistical software. Biological and Medical Data will be used for the analyses.

Pre-requisite: Instructor's permission or any Statistics course open to both undergraduate and graduate students.