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Exploratory Data Analysis

Created/Published/Taught by:
Coursera
Johns Hopkins University
Roger D. Peng
Jeff Leek
Brian Caffo

Content Found Via:
Coursera

Free? Partially: Some Free Content, Some Paid

Cost: $0.00

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This is course 4 of 10 in the Coursera Data Science Specialization.

From Coursera:

“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.

Syllabus:

  1. This week covers the basics of analytic graphics and the base plotting system in R. We’ve also included some background material to help you install R if you haven’t done so already.
  2. Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualizing data, it was part of the original R system and lacks many features that may be desirable in a plotting system, particularly when visualizing high dimensional data. The Lattice and ggplot2 systems also simplify the laying out of plots making it a much less tedious process.
  3. Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for exploratory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dimensional data (many many variables). We also cover novel ways to specify colors in R so that you can use color as an important and useful dimension when making data graphics.
  4. This week, we’ll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data.”

Recommended Prerequisites: Some programming experience

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