Chevron Left
The Data Scientist’s Toolbox に戻る

ジョンズ・ホプキンズ大学(Johns Hopkins University) による The Data Scientist’s Toolbox の受講者のレビューおよびフィードバック



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
Introductory course



Sep 08, 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.


Apr 24, 2018

This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.


The Data Scientist’s Toolbox: 3726 - 3750 / 4,213 レビュー

by Geoff S

Jul 27, 2016

This is a required course in the Data Science specialization. If you're planning to take that specialization, you should start with this course. If not, you shouldn't bother - there's not much here.

by Michal Š

Aug 12, 2016

As a software engineer I found this course a bit useless, but I do understand other people don't have the same background. Having said that, I believe it should be optional in the specialization.

by Yueming P

Nov 18, 2017

It's a great course. But the peer-graded setting of projects really delays the grant of certificate. It took me more than two sessions of waiting just to get a grade for the peer-review project.

by Michele B

Sep 13, 2018

Very basic, can be done in one day! However, we always need something simple to begin learning something new. Also, it was useful to have some guidance in the installation of the various tools.

by Alan Z

May 29, 2016

The course is basic, and quite simple. Some might say too simple. I think it should be about 1 week long. I already knew all the things going into it so it was easy to finish it over two days.

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 Olexandra M

Nov 27, 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 swati

Feb 07, 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 George C

Jul 05, 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 05, 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 breana m

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 01, 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 22, 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 22, 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 M

Dec 06, 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 Christian S

May 07, 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 03, 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.