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Learner Reviews & Feedback for The Data Scientist’s Toolbox by Johns Hopkins University

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34,048 ratings

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 RStudio....
Highlights
Foundational tools

(243 Reviews)

Introductory course

(1056 Reviews)

Top reviews

AK

Mar 11, 2020

I would like to thank Coursera and Johns Hopkins University for the course, I learned new things in the course which I think is the next step which can help me to accomplish my future goals.Thank you.

LR

Sep 7, 2017

It was really insightful, coming from knowing almost nothing about statistics or experimental design, it was easy to understand while not feeling shallow. Just the right amount of information density.

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By Enrique B

Dec 4, 2015

I'm doing this training for the second time, now as a beta-tester. Particular comments about lecture content, problems, etc. have been put in every lecture.

General comments, in short:

1) Related to the new platform and UI design:

_ It is cleaner and simpler than the previous one. I like it, BUT...

_ It lacks of some useful features: saving intermediate results in quizzes before submit them; calendar; limited number of subforums.

_ The most relevant flaw: there are not downloadable versions of lecture slides. Unacceptable! No way to check most of the links we saw in slides (URLs not visible).

_ Description and steps in course project appear "too packed" together. I prefer the former design.

2) Related to content:

_ The course is mainly for preparing students for the rest of data science specialization program. When you said "toolbox" you mean the concrete toolbox you will need to do the program. Some people expect to have a general introduction to data science but that is only a half of the content. I think this is clear enough in the presentation but for some reasons there are people in forums who protest the content, so maybe you should insist more in this fact.

_ I would like to suggest some kind of reorder of material: week 2 is all about installing a running tools and week 3 about key aspects of data analysis. Maybe you can split both types of content between wk2 and wk3 to make wk2 more appealing for not technical oriented students.

_ Git is a source of problems for a good portion of people. See my comments in lectures about how Git is explained.