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

32,679 ratings
6,973 reviews

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....
Foundational tools
(243 Reviews)
Introductory course
(1056 Reviews)

Top reviews

Apr 14, 2020

As a business student from Bangladesh who is aspiring to be a data analyst in near future, I love this course very much. The quizzes and assessments were the places to check how much I exactly learnt.

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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6251 - 6275 of 6,862 Reviews for The Data Scientist’s Toolbox

By Mohammad O

Nov 28, 2021

I like to continue, but i stopped learning because the robot instruction which is annoying presentation.

I'm looking other courses which has better presentation tools, like real instructors.

By Constantin S

Feb 10, 2016

It's a very basic introduction. Would be nice to have the option to skip it for more advanced students.

Also the recorded lectures are really quite, so you have to turn up the speaker a lot.

By Pratheek G

Nov 16, 2017

Good Introduction to the basic tool set, Can be bit more comprehensive by giving an overview of other tools available to do the same operations and also why this tool set has been chosen.

By Fraida L

Nov 25, 2016

There wasn't much to learn that I didn't already know. There was a lot of talk but no real practical application (I guess except for github for people that didn't know how to use that).

By Sasha M

Nov 26, 2017

This course coud have been provided as intro to R programming course or it could be just planned for 1 week. Anyway, overall it was ok though not yet extremely intense or informative.

By Lauren O

Apr 13, 2017

It was a good overview. I understand that it’s really just a quick intro into upcoming specialization courses. I wouldn’t take it alone. But I’m excited for the rest of the courses!

By Emmanuel A

Jul 10, 2020

I got a brief understanding of R. Better something than nothing. The problem is, they need to update their lectures. This would be less confusing and more convenient for students.

By Christopher H

Aug 13, 2021

I know it might be the future for training videos but had a hard time staying concentrated listening to a mono tone computer generated voice. The course content itself was good.

By swati

Feb 7, 2018

Make it more illustrative.. The examples are very less and not explained properly for beginner. Assignments should be more practical. There must be a tutor to clarify the query.

By Abhijit H D

Sep 25, 2017

A good attempt to give introduction on basic Data Science tools though most videos end abruptly which spoils the seamless navigation experience. Maybe the team can work on this.

By Justin B

Sep 15, 2016

Good introduction, but the professor could have been more descriptive about how to use git/github. These are new concepts for people so it may be difficult to grasp right away.

By James S

Mar 28, 2017

Probably a good course if you aren't terribly familiar with GIT or some form of version control. It also helps to get all the tools in place to be ready for the other courses.

By Jeff J

May 25, 2020

I understand the rationale for the auto-voiced videos, but I wasn't crazy about it. More importantly, I didn't understand why the text-only versions can't be used on an iPad.

By George C

Jul 5, 2017

Good outline but perhaps a bit slow going when a lot of people want to get into coding as quickly as possible- could some of the git instructions come further down the line?

By Peter P

Mar 4, 2019

The automated videos have seriously reduced the value for money for this course. With thousands of students, surely you can spend a bit more time making this presentable.

By Christian B

Jan 15, 2018

Good course for an absolute beginner, but much too light if you have any experience at all with data science or programming. Entire course completed in just a few hours.

By Bre K

Aug 11, 2016

It is a good intro course to get set up with everything you may need for future courses, but it's not necessary if you are already a little familiar with github and R.

By Chaitanya A

Jun 22, 2017

The assignments were too simple to solve. Maybe 1 or 2 graded questions on Git/GitHub could have been added considering the importance of its usage in future courses.

By Chrissie J

Feb 1, 2016

I enjoyed the start up course and look forward to more, but am battling to figure out how to sign-up for the next step all roads seem to force me back to the Toolkit.

By Sam K

Jan 21, 2019

Nice to have a place to get all the tools setup but it's also harder to feel like it's worthwhile when there are no applications for any of the things we installed.

By José A d C F

Sep 21, 2019

I think final assignment can be improved. For example, the assignment implies that you know how to generate a and the video classes doesn't teach that.

By Juan I Z

Dec 6, 2017

For people that are new to Data Science this is a good intro, for people that might have some experience with R, statistics and ML in general this is way to basic.

By Jaydipkumar D P

May 9, 2020

The course is structured well, but I found it too elementary, I didn't learn anything new. So, If you know basics of Data Science and R. Kindly skip this course.

By Christian S

May 7, 2016

I think this course could be integrated into the other ones of the specialization, or, if it is meant to be just a course to get an overview, be free of charge.

By Biplab G

Jun 3, 2017

Helps to get the initial environment setup for the Data Science specialization.

Certificate received after completing the course is not effective/useful at all.