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.
Exploratory Data Analysis
Exploratory Data Analysis
This course is part of multiple programs.
Instructors: Roger D. Peng, PhD
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4.7
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54 hours to complete
3 weeks at 18 hours a week
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What you'll 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'll gain
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Assessments
2 assignments
Taught in English
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There are 4 modules in this course
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.
What's included
15 videos6 readings1 assignment5 programming assignments1 peer review
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.
What's included
7 videos1 reading1 assignment5 programming assignments
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. All of this material is covered in chapters 9-12 of my book Exploratory Data Analysis with R.
What's included
12 videos1 reading4 programming assignments
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.
What's included
2 videos2 readings1 programming assignment1 peer review
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4.7 (205 ratings)Offered by
Recommended if you're interested in Data Analysis
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Showing 3 of 6071
LS
4
Reviewed on Oct 31, 2019
Good introduction. The swirl exercises kind of reproduce the lectures though- felt like it might not have been the most efficient use of time to go over the exact same example again.
EK
5
Reviewed on Jun 5, 2020
Awesome course that expands on your R knowledge. Only nitpick is that some of the links don't work and the videos need an overhaul as there seem to be little to no updates since 2015/2016.
IA
5
Reviewed on Jan 17, 2016
Very nice course, plotting data to explore and understand various features and their relationship is the key in any research domain, and this course teaches the skill required to achieve this.
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