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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....
Introductory course
Foundational tools



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.


Jul 22, 2017

Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.


The Data Scientist’s Toolbox: 3851 - 3875 / 4,087 レビュー

by Drake O N

Mar 17, 2017

Good starter setup. Looking forward to the technical portion of the Specialization.

by Pragati

Mar 03, 2016

Students be more interactive.

by Aleksander O

Sep 10, 2018

all good for beginning

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 Rajeev R J

Sep 15, 2018

Didn't get an awful lot from this course. The videos have a lot of information which are not directly related to the course.

by Matthew W

Aug 11, 2018

Generally good information, but the static powerpoint videos are a bit too vague to be useful. I ran into issues several times when the steps described in the video (sometimes providing incomplete terminal or git commands) didn't coincide with the steps described in site specific tutorial videos recommended in the course forum. So I ended up spending a lot of time 1) figuring out the full commands required, and 2) reconciling conflicting sets of instructions after receiving error messages. This mainly occurred when trying to get my local git work to correspond to GitHub. I suggest more actual demos in the video lessons (i.e., actually type in full commands, show the result, and explain how to interpret those results), or 2) explain overarching concepts and then simply list a set of existing online tutorials that should be followed.

by Ove R

Aug 29, 2018

Candid but respectful comments......

Some of the lecture material seems outdated. What we are seeing is often different than what is being presented. Is your content up to date? Lectures are quite good. When we have a serious technical issue, who can we reach out to for quick assistance? In my case, for some reason when I attempted to download and open R Studio, download was fine, the file executed as expected, but the application was nowhere to be found on my computer. This is not good because I can't begin R pranking without it. I have reached out vendor and am waiting for response. Concerned. Other than that am liking what I see

by lcy9086

Aug 28, 2018

Everything is fine

I think they had better not include the GitHub thing in it without clear explanation.

It takes me too much time on that assignment

by Sadanand U

Aug 19, 2018

Very basic, may be a bit more use cases on Git would have been useful. But that's just me

by Ahmad A

Oct 07, 2018

the voice is too low and the level of instructing are not for beginners, but with a lot of effort you could pass

by Dinar

Oct 18, 2018

To easy :) but good start for the beginners!

by Anushree V P

Oct 25, 2018

The course structure is really good. The content is good too. I found the speed a little too fast. Plus there should have been some small exercises in between before the quiz to make the lesson more interesting and intriguing. Another point that I would like to state is that, the slides could be even better and visually appealing than they are now.

by Nicolas B P

Oct 04, 2018

Not sure if we will come back to Git, but i thought that in this section it was covered way too superficially. Maybe the idea was that we should get to it ourselves, but i guess my expectation was different. Other than that, the course was ok.

by Chris C

Nov 01, 2018

There has to be a more engaging way of introducing course material. For example, by showing someone actually using these commands in the videos vs. just putting the Git, R commands in a power point bullet format.

by Alex K

Nov 07, 2018

I took the survey.

by Ryan W

Aug 21, 2018

As an intro, this course is probably pretty good. I, however, already had experience with R (although the refresher was useful). However, if you've taking a data science or machine learning course recently, I'd give this one a pass and head on to the next course.

by Shawky A

Aug 23, 2018

we need more explain in this videos

by Sourav P

Jul 29, 2018

One Could have introduced the basics of R in this course..The course was too easy..Even for beginners

by Ahmad S M

Jul 29, 2018

Not very good.

by Jake P

Apr 20, 2019

The lectures are sometimes needlessly long with a lot of superfluous talking. The course would be better with more concrete examples and THE OUTPUT OF EACH INCLUDED. The course explains very simple queries and then asks you to do complex ones in the quizzes when the examples were poorly explained. Khan academy is a much superior course to this one, yet it does not offer a course certification. If this course actually wants to teach people efficiently it should emulate the real-time learning and coding in browser that Khan Academy has.

by Nikolay B

May 06, 2019

Hi guys. I'm not sure that you are reading the feedback, but instead of saying that it's good or bad I'm going to come up with suggestion. I'm data pipeline architect with 20+ years of experience who decided to take these courses to understand a gap of knowledge that current data scientists have. I think it comes from the very course. The R is kind of out dated for current world of big data, but I think you've already heard about this. Some Data Scientists who show up in our company they are good with theory but very bad in implementation. They don't understand the Big Data, especially distributed data. R is good language to the lessons but it doesn't have any connection with real world. You better include some basic knowledge about Spark (especially Spark ML), distributed computation and finish with R. Most of data science algorithms and libraries implemented (and used by real world) are in Python. Contact with some Cloud Providers like AWS and Google to create accounts for education. You course would be more attractive and, what is most important, would be more useful for people and companies where they start to work.

If you want, you can contact with me about some volontier consultancy. My email is

by Suhas K

Apr 24, 2019

I wish there were more exercises for the project.

by Tanvi M

May 25, 2019

This course lacks the inter-activeness that holds up a class. Even the material was not worth the money as it just teaches you to install certain programs and exactly what one can do with it. I feel there is enormous scope to improve this course in particular.

Things I will suggest:

1. The installation process is not shown with a depth. I feel increasing video size wont matter as reducing and removing certain important points that students should learn. I hope a better depiction and graphical representation of such an amazing subject can be done.

2. The problem with coding is that though they told how to make certain thing bold or Italics not actually it was shown as to where to put this.

I hope that everyone gain interest in such a subject.

by Debanjan D

May 25, 2019

Okayish course. This course will give you an introduction in RStudio and Data Science.

by Anton K

Jun 16, 2019

I don't agree with the order at which this course is introduced to learners. Why do we need to learn CLI and Git at the very beginning? Besides, everything in this course was sort of detached and sketchy. For instance, the intro to the types of analysis (e.g. descriptive, exploratory, inferential, and so on) is not covered well. In my opinion it would be much better to have an intro about the underlying theories and concepts in much more detail rather than learn Git command or learn how to melt and cast data (from Hadley's presentation).