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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
21,643件の評価
4,328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
Introductory course
(1056件のレビュー)
Foundational tools
(243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: 3651 - 3675 / 4,203 レビュー

by Chengming X

Aug 14, 2019

I have to say the text to voice translation of the text to video lacks good rythm, sometimes it is not easy to follow all the detial espatially there never is natural pause after some touch ideas or steps to me. As a class of introdution level to layman like me I think it could be better introducing more practical examples to practice, or I would like to see some links to external study materilal, that would make the class experience even better to avoiding frequently searching for troubleshooting.

by Sanket B

Jun 15, 2019

The initial lectures were good . The Git & Github part got me little confusing , a little detailed explanation with live examples would have really helped. The last conceptual part was interesting. Some reading material just to drill down on certain Data science jar-guns would really help though it is understood that best place to find answer to question is google / forums / stack overflow...Still some reading material would really be very helpful to maintain the interest in the course.

by Vicki K

Mar 21, 2016

Basically if you take this course you are paying money to create an account on a website and download some software (both of which you can do for free). The rest of it is a preview of the other courses in the series. The quiz questions don't correspond to the information on the slides. I successfully passed the course, but I didn't really learn anything. Now I am debating on whether or not to continue to the R programming course after reading through the reviews of that course.

by Andrew H

Mar 12, 2017

This is a good, general introduction. A motivated student can run through it very, very quickly. As the first of ten courses, I understand that it is a very general introduction. Still, I think it could be ramped up a bit. Week 2 of the next course - R programming - is kind of a kick in the head if you're not a programmer. I feel like some of the content from R programming could have be included in the toolbox course in order to take advantage of the relatively light load.

by John A

Mar 28, 2016

This should be at most a 1 week course, that is free. Half the course is installing Rstudio and signing up for github. The other half of the course is simply learning what each course down the pipeline is about. Those lectures could just be tacked onto the description of each course and you would get the same thing out of it.

I think this course would be improved by more instruction on what git is and how to use it and maybe going over some fundamental statistical topics.

by Gavi D

Jan 02, 2020

The course was exceptionally planned and executed. One big problem that I had with the course were the automated videos; I can't say for others, but I wasn't at all comfortable with an AI voice teaching me the course content. I don't think I can ever get used to that. I would rather take a course that's 10 years old, 80-90% valid but has a human teaching me the course content. Other than that, I enjoyed the course. Thank you!

by Molly H

May 17, 2017

This course accomplishes what it says it will, but boy is it boring. If you are not already experienced in data science, it also requires a fair amount of imagination to picture what all these tools are actually used for. I would have preferred to have these tutorials integrated with the more substantial courses in the specialization. That way I could see how these tools fit in to an actual project.

by Ilkka N

Jun 02, 2019

The course dealt with basic software issues on getting you ready for Data Science, and discussed briefly more conceptual topics. The contents of this course by no means would take 4 weeks to complete from anyone, so I think the time span to take this course is exaggarated. Still, it is very important course to get you started, if you are complete stranger to R, RStudio, GitHub and R Markdown.

by Allen D

Apr 27, 2017

There is a pretty big jump from the content to actually completing the assignment. The assignments are not well aligned with the swirl learning or the videos. There is no logical process taught about how to move forward if you get stuck. It often means a student is forced to search the internet and hope the answer they find is appropriate so they can write their own code.

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 Jairaj A P

Aug 26, 2019

i felt this course was very disorganized. It introduces terms and concepts not explained before. There was an assignment on creating forks. This process was not in any lecture. Of course, with R and GitHub you can find anything on internet.

The lectures narrated by Amazon Polly is very boring. It also messes up some of the terms.

by Lou O

Jun 21, 2016

It's ok. After the first lesson, I should be able to provide a clear elevator pitch with a high level understanding of what I can expect to accomplish (4 or 5 steps) as a Data Scientist. Instead, there was one slide that touched on this quickly, somewhere in the middle. What are the problems, how do I solve them, give samples.

by Sandro G

Sep 21, 2016

The first course is composed in articulate way that allows a simple and schematic way of comprehension, but some single parts of the first course seem to be lacking of some information, above all to me without previous experience in informatics tools like github. Maybe I suppose to master this tools too long in advance.

by Ashok N

Mar 13, 2019

literally i lossed the feeling of real time learning and it seems like just reading. i really do not like this kind of teaching style. infact direct teaching by the instructors is being a good experience rather than using this kind of technology

i reas all the course content, without listening by recorded speech

by STEVEN V D

Nov 15, 2017

Good introductory course for the specialization.

Video' probably need an update as they're all cut in the end.

Also some more background and a little more extensive lectures would have been nice.

Anyhow, it did the deal: an introduction to R, RStudio, Github and Git.

Curious what the following courses have to offer.

by Nguyen N T

Jan 21, 2020

The course size is pretty small compared to other courses I joined in Coursera. It took me only 3 days to complete the 3 day course. I think all setup guides should be left as assignments for students with some links where we can refer to on our own. Anyway, the course finally convinced me to start using R.

by Mohamed H

Dec 15, 2016

Instructor speaks very fast so that i read subtitles instead of hearing what he say, in addition to i stop video more times to understand what he say, but totally the scientific and technical contents are great also his advises for us in which how we can find the answers for our questions about data science

by Jose O

Feb 06, 2016

The part of explaining Predictive and Inferential Analysis is confusing. I think it won't hurt to give some more specific examples and methods used in each case. Both types of analyses involve sampling, so I think it is necessary to keep it clear how that sample can be used to either "infer" or "predict".

by Fred P

Mar 06, 2016

the lectures are full of the Prof misspeaking, this leads to you not knowing how to complete the task because the Prof can NOT communicate properly to us while we listening to the lectures... it seems like they completely missed the fact that NONE of us are data scientists...

Now your Audience......

by Alejandro O

Dec 17, 2017

I put three stars because it should be specified more how basic this course is, is almost that this is done for somebody that doesn't know almost anything from CS. So it should integrated with other kind of specialization. I hope that the following courses have some serious math and advance topics.

by Donald J

Nov 12, 2016

The course goes over the basic toolkit for data scientists. Overall it seemed too easy and maybe a bit simplistic. I was expecting more. There was a lot of optional reading made available in week 1, perhaps some optional assignments/quizzes related to that reading could be added to the course.

by Diego N L

Sep 23, 2016

concepts were very good but teaching method/material must improve...some of the materials and methods used are too unstable to be useful for professional use...more work should be done by instructors to separate the 'reliable' concepts/info from 'interesting to know but not ready for mass use'

by Steven M

Feb 12, 2016

Very basic material, but a good introduction and a necessary step to ensure a baseline of knowledge for future courses in the data science specialization. I would only take this course if you are interested in the specialization otherwise save your money and google the info you need.

by Noah M

Feb 11, 2016

Insufficient available project available for review and thus unable to pass course due to technicality. This is a major problem. The course should still be passable even in the absence of sufficient other projects to review, which is a problem that no student has any control over.

by Dane S

Sep 08, 2017

I was a little put off by having to grade my peers and it felt like the final task required a few bits of information that hadn't been previously covered. I felt some more examples could be useful in getting people adjusted to GIT. Not a bad first course but not what I expected.