## Introduction

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem 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 alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

<a href="http://www.youtube.com/watch?v=nwFyIxc4AE0" rel="nofollow" target="_blank"><img src="http://img.youtube.com/vi/nwFyIxc4AE0/0.jpg" alt="0" title="How To Choose The Correct Channel Type For Your Video Content " /></a>

### What you'll learn:

• Random variables
• Distributions
• Inference: p-values and confidence intervals
• Exploratory Data Analysis
• Non-parametric statistics

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

The Data Analysis for Life Sciences courses are self-paced and include:

## Meet The Author

### Rafael Irizarry

Professor of Biostatistics, Harvard School of Public Health

Dr. Irizarry received his bachelor’s in mathematics in 1993 from the University of Puerto Rico and his Ph.D. in statistics in 1998 from the University of California, Berkeley. He joined the faculty of the Department of Biostatistics in the Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. Dr. Irizarry has worked on the analysis and pre-processing of microarray, next-generation sequencing, and genomic data, and is currently interested translational work, developing diagnostic tools and discovering biomarkers. 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.

### Michael Love

Postdoctoral Fellow, 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.

Data Science

Data Science

Data Science

Data Science