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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

4.5
21,025件の評価
4,198件のレビュー

コースについて

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....
ハイライト
Foundational tools
(243件のレビュー)
Introductory course
(1056件のレビュー)

人気のレビュー

LR

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.

AM

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.

フィルター:

The Data Scientist’s Toolbox: 3876 - 3900 / 4,067 レビュー

by Florence C

Nov 08, 2018

I wonder if most of students who took this course would have expected to keep installing software, one after one, and seems never end. For the whole week course, there is nothing related to the course. The worst part is that once I got into trouble to get a software work, I got stuck. I spent hours and hours to search for a solution on the web. However, most of the answers are too technical for me to understand. Although I understand that data scientists have to use some computer programs to assist them to the work done, I don't think that, as a novice, at this stage I need anything like GitHub or git to help me learn the subject. Moreover, I'm not sure if I would be taking another data science course yet.

by Arun R

Jan 07, 2019

Very basic - Did not teach all the necessary things required with Git.

by Marie J

Feb 26, 2019

I found this informative but you do not get much in terms of service. One of the quiz has a technical issue and I made a complaint weeks ago, but I still haven't gotten a response. Nor has it been fixed.

by Abdallah M A

Mar 02, 2019

The instructor is an automated speaker, it was too bad, I couldn't focus at all.

by Javier R L G

Aug 06, 2018

Too slow!! Why waiting for others to finalize and rate, enter enough dummy exercises so that the student can complete the course much sooner. Thanks.

by Ligeng X

Sep 25, 2018

Waaaaaaaaaaaaay too easy

by Amy-Louise S

Sep 24, 2018

The lectures were very uninspiring for the most part and I felt that my practical understanding was poor. The forums were also not particularly helpful as I saw Moderators mocking students for asking valid questions based on their inexperience with Data Science. I spent most of my time finding better tutorials on YouTube, so really.... I have a certificate but don't owe much of that to the course in question. It only becomes worse as the complexity increases with the rest of the Courses in this Specialization.

by Hassan A

Oct 04, 2018

I think it was too long for such content

by Todd H

Jul 15, 2018

I am simultaneously doing a course here and one on Udacity. In comparison, this course is much less enjoyable and efficient. There are several reasons for this 1) you never see a person present the material in the videos, it is just pictures of slides. This makes it feel less personal and enjoyable. 2) The lessons are relatively long and there are not opportunities to practice in between. Udacity, for example, has short 1-2 minute videos for each concept and then ungraded practice quizzes, which make it very clear what you are supposed to learn without feeling like you failed if you make mistakes. This saves you time so you don't have to go back into a 15 minute video to hunt for an answer to something. 3) The quizzes here test a lot of minutae. They are not always focused on the main points from the lecture. This can be frustrating because you may feel like you need to spend an unreasonable amount of time looking for answers to small points or get frustrated and feel like you are failing. That does not really make sense for an overview course. 4) The instructor often rambles or has side remarks. He sometimes forgets to give the proper background information for beginners. Takeaways or action points are not always clear. 5) This course feels unnecessarily long for what it is supposed to accomplish. It could be a list of programs to download and a very short summary of things you will learn in upcoming courses. Instead, you spend hours on this and feel like you walk away with no new technical skills.

by Katherine F

Mar 17, 2016

Too short and easy.

by Karsten T

Feb 15, 2016

This could probably be compressed into a single week of setup for the R Programming course. There isn't much in terms of substantive learning here.

by Vlad D

Feb 27, 2017

Too basic!

by Selina H

Apr 12, 2016

It's more a preview than an actual class.

by Ben V

Sep 13, 2016

Very very introductory. I didn't find the tooling aspects of this course particularly helpful, but I'm not in the target audience. It's length was misleading -- I completed the work in two days easily, but I am a technologist, and already had the tools installed. If you use GitHub and RStudio, the meat of the course is only about an hour of the lecture.

by David C J

Sep 26, 2016

This was really basic information. It's not really a 4 week course. It takes several hours. The info is necessary, but it's sufficient as a single week of content for a larger course.

by Anna D

Mar 04, 2018

It's great to learn how to use GitHub but this hardly qualifies as a course.

by Ignacio J S

May 23, 2017

The course is extremely introductory and even though it may lead you to references you may use to self-teach yourself, it is not worth taking a four weeks course for a one week content. At the end of the week you will have about three new programs installed in your computer and no idea on how to use them for practical situations. Although it's intended as introductory, it surpasses that barrier to mere spectacle.

by Ashish S

Jan 24, 2017

ok

by Tarunoday S

May 06, 2017

This module should not be kept as a separate piece...The steps mentioned in the lectures does not properly cover what is asked in the assignments.The lecture content needs to improve to cover the assignment

by amaru f

Apr 25, 2018

i think was ok to start up

by Kristin N

Jun 07, 2016

Jumps around, doesn't put anything into "real world" examples.

by Lenka " P

Apr 19, 2016

It is a nice introduction, but the material is barely worth a one week orientation lesson (I have completed it in about 4 hours with watching all the videos, taking detailed notes, and completing the quizzes for 100%) . Why should we pay for a specialization preview, we can read info about the separate courses on our own? I am really looking forward the next courses in the specialization since I really need a good course in statistics and this seems to be one, but the first course should be eliminated. The installation of R and github account videos should be added to the next course as an introduction.

by Anit B

Jun 27, 2016

Introductory Course mostly around installation and setup for the other courses in Specializations

by valentine

Aug 27, 2016

Would like to have seen more material using git and git bash commands. More repetition here would be helpful. Seems like a lot of this information will be lost or forgotten when it comes time to use it, especially as it relates to the Data Science Specialization.

by Augusto U

Feb 18, 2016

Very basic introduction to Data Science. Seasoned programmer or other IT professionals are likely to find this course really weak.