Details
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 here http://harvardx.harvard.edu/research-statement.
Outline
- R scripts to conduct reproducible research
- The connection between concepts and implementation by programming in Rhe basics of statistical inference
- How to compute p-values and confidence intervals and implement basic data analyses
- How to use visualization techniques in R to explore new data sets
- How to determine when to apply robust statistical techniques
Speaker/s
- Professor of Biostatistics
- Harvard T.H. Chan School of Public Health
Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan 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
- Postdoctoral Fellow
- Harvard T.H. Chan School of Public Health
Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan 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.
