Exploratory Data Analysis
Johns Hopkins University · View on Coursera ↗
Taught by Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Data Science
About the 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.
The dissent
The most-upvoted critical review (14 votes, 2.0★).
“# Lectures are mostly powerpoint karaoke along the lines of "You can do that thing. And you can also do that other thing. And also you do this third thing" without much real-world application.”
Coursera's 4.7 comes from 6,093 ratings. Of those, 1.2% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗