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生命科学中的统计学与R编程

Statistics and R for the Life Sciences


An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.


About this Course
We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals. We will provide examples by programming in R in a way that will help make the connection between concepts and implementation. Problems sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternative when data do not fit assumptions required by the standard approaches. We will also introduce the basics of using R scripts to conduct reproducible research.

Topics:

Distributions
Exploratory Data Analysis
Inference
Non-parametric statistics
This class was supported in part by NIH grant R25GM114818.

This course is part of a larger set of 8 total courses:

* Registration open through 27 April 2015

* Classes start Jan 19; all assignments due by 23 May 2015


PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Advanced Statistics for the Life Sciences

PH525.4x: Introduction to Bioconductor

PH525.5x: Case study: RNA-seq data analysis

PH525.6x: Case study: Variant Discovery and Genotyping

PH525.7x: Case study: ChIP-seq data analysis

PH525.8x: Case study: DNA methylation data analysis

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

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Watch the Course Intro VideoStatistics and R for the Life Sciences
School: HarvardX
Course Code: PH525.1x
Classes Start: 19 Jan 2015
Course Length: 4 weeks
Estimated effort: 6 hours / week
Prerequisites:

Basic programming, basic math.


Course Staff
Rafael Irizarry
Rafael Irizarry
Rafael Irizarry is a Professor of Biostatistics at the Harvard School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.

Michael Love
Michael Love
Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard School of Public Health. Dr. Love received his bachelor’s in mathematics in 2005 from Stanford University, his master’s in statistics in 2010 from Stanford University, and his Ph.D. in Computational Biology in 2013 from the Freie Universität Berlin. Dr. Love uses statistical models to infer biologically meaningful patterns from high-throughput sequencing data, and develops open-source statistical software for the Bioconductor Project.

Prerequisites
Basic programming, basic math.

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