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.