About this course: 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.

Created by: Johns Hopkins University

Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health

Basic Info	Course 4 of 10 in the Data Science Specialization.
Language	English, Subtitles: Chinese (Simplified)
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.6 stars Average User Rating 4.6See what learners said

Syllabus

WEEK 1

Week 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.

15 videos, 6 readings

Graded: Week 1 Quiz

Graded: Course Project 1

WEEK 2

Week 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 par...

7 videos, 1 reading

Graded: Week 2 Quiz

WEEK 3

Week 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 dime...

12 videos, 1 reading

WEEK 4

Week 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. How one goes about doing EDA is often personal, but I'm pr...

2 videos, 2 readings

Graded: Course Project 2

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Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

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Ratings and Reviews

Rated 4.6 out of 5 of 2,127 ratings

Excellent course!

The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.

Great course, an excellent instructor who makes the videos easy to watch and listen.

Assignments were very important to me, as I learned a lot while doing it.

Great course. This course required us to create multiple plots using different R libraries created for the purpose. Although ggplot2 seems to be very popular, the base plot system and the lattice plot system provide compelling alternatives.

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