Master Exploratory Data Analysis

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In this course, Master Exploratory Data Analysis we will cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

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About this Course

This course, Master Exploratory Data Analysis 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.

What You Will Learn

  • Understand analytic graphics and the base plotting system in R
  • Use advanced graphing systems such as the Lattice system
  • Make graphical displays of very high dimensional data
  • Apply cluster analysis techniques to locate patterns in data

Skills You Will Gain

  • Cluster Analysis
  • Ggplot2 in Master Exploratory Data Analysis
  • R Programming
  • Exploratory Data Analysis


19 hours to complete

This week covers the basics of analytic graphics in Master Exploratory Data Analysis 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.

15 videos (Total 109 min), 6 readings, 7 quizzes

17 hours to complete

Welcome to Week 2 of Master 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.

7 videos (Total 61 min), 1 reading, 6 quizzes

13 hours to complete

Welcome to Week 3 of Master 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. All of this material is covered in chapters 9-12 of my book Exploratory Data Analysis with R.

12 videos (Total 77 min), 1 reading, 4 quizzes

5 hours to complete

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. How one goes about doing EDA is often personal, but I’m providing these videos to give you a sense of how you might proceed with a specific type of dataset.

2 videos (Total 55 min), 2 readings, 2 quizzes

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Mass Communication Student, Federal University, Oye Ekiti and Content Creation Specialist. Join me on Twitter.

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