The Data Scientist’s Toolbox
Johns Hopkins University · View on Coursera ↗
Taught by Jeff Leek, PhD, Roger D. Peng, PhD, Brian Caffo, PhD
Data ScienceMath and Logic
About the course
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStud
The dissent
The most-upvoted critical review (85 votes, 1.0★).
“Putting this into Specialization requirements is smart from your perspective, you are basically saying if you want to reach Capstone pay me $50 more, but at least fix the typos you made during video, just a little respect to your subscribers. But right now, I highly doubt that Capstone Project will be something serious that I want to mention in my Linkedn. There is also downside of what you do. But since you are in between the top rated courses either nobody uses Coursera anymore or people are silent enough and patient enough.”
Coursera's 4.6 comes from 34,113 ratings. Of those, 2.1% 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 ↗