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Covers the basics of R software and the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarrays and other platforms.

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The day-to-day collaboration between the researchers in Public Health and Biostatistics at the School reveals unified topics that cut across many applications.

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Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data.

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Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples.

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Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples.

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Statistical Reasoning in Public Health provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions.

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Statistical Reasoning in Public Health II provides an introduction to selected important topics in biostatistical concepts and reasoning through lectures, exercises, and bulletin board discussions.

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This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences.

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This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences.

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Presents construction of sampling frames, area sampling, methods of estimation, stratified sampling, subsampling, and sampling methods for surveys of human populations. Students use STATA or another comparable package to implement designs and analyses of survey data.