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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

4.6
32,988件の評価
7,046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....
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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

SF

2020年4月14日

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.

LR

2017年9月7日

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.

フィルター:

The Data Scientist’s Toolbox: 4651 - 4675 / 6,942 レビュー

by RONIT Z

2021年10月26日

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by DIBYARANJAN P

2020年8月11日

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by GOKULAN.M

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by Parth M

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by Jonathan A F

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by Veene V

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by Marco G

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by Beatrice S

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by Govind R A

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by Kyle D

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by Rogelio N

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by Sujana M

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by Park, M

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by Andy N

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2016年3月4日

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by Shweta_Jha

2016年2月8日

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by Enrique B

2015年12月4日

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.

by Krish H

2020年4月27日

So why not 5 *'s - because I could not give 4.93 *'s

What I found excellent and thus 5 *'s - *****

a) material

b) even the automaton of a voice - was not a deterrent but rather soothing - oh well tells you something about me!!!

c) Material is deceptively easy in the front end and gets progressively more difficult later

d) the references are well thought out even of the entry into Data science

e) The build up is very logical

f) Lots of thought has been put into the design by the team

What I found lacking and thus dinged a couple of points (perhaps too harshly)

1) The mini quizzes do not sufficiently force you to think about the material and thus easy to breeze thru to the next week - perhaps I am being too judgmental and it may improve in the next course of the specialization

2) I could not see an easy way to get the material to review when taking the test - most of the time I forced myself to not look at the material to test myself but the onus is on you

3) It should not be about getting some questions wrong but learn the material so that every question can be right (imparting knowledge vs getting a certificate with 80% pass - think how would we feel if this was a training for a neurosurgeon ;) ) - a suggestion would be to force the student to read the section that pertains to the incorrect answer and not allow the test to be taken again until that is accomplished - like in a class room setting.

by Isabelle A M B F

2020年7月21日

This course is a great intro to the potentials of R and the world of Data Science and Big Data as well as the approaches and mindset needed for it. It's fairly straightforward, my only suggestion is to maybe include some tips on troubleshooting some installations for some parts of the lectures. For example, I already had R and Rstudio installed from my college days, but the versions were outdated (R 3.2.3) and weren't compatible with some packages and they weren't working but I wasn't understanding why until I had to google it. Similarly, I had some issues with linking my GitHub account to my Rstudio because the route it was using wasn't working and the correct one was highly similar, I was only able to fix it thanks to forums. These details can be frustrating for someone who's trying to follow along with the lecture but is stuck, so thank god for forums. It would be nice if the instructor could write a couple of tips on how to fix some common issues like those for novices.