Posted on Jan 15, 2018 | Rating
   
  

Exploratory Data Analysis

by 1, 1, 1
  • 1 The Johns Hopkins University

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Course Description

In this course you will learn the ideas of reproducible research and reporting of statistical analyses. Topics covered include literate programming tools, evidence-based data analysis, and organizing data analyses. In this course you will learn to write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, publish reproducible documents to the web, and organize a data analysis so that it is reproducible and accessible to others.

Schedule

  • Structuring and organizing a data analysis
  • Markdown and R Markdown
  • knitr / RPubs
  • Reproducible research check list
  • Evidence-based data analysis
  • Case studies in air pollution epidemiology and high-throughput biology

research design

Publisher:
The Johns Hopkins University

{
  • author = {Roger Peng and Jeff Leek and Brian Caffo},
  • title = {Exploratory Data Analysis},
  • publisher = {The Johns Hopkins University},
}
Roger Peng and Jeff Leek and Brian Caffo Exploratory Data Analysis The Johns Hopkins University
teaching principles

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