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Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

4.6
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Оценки: 3,914
Рецензии: 561

О курсе

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results....

Лучшие рецензии

AA

Feb 13, 2016

My favorite course, at least it gives me an argument why scripted statistics is awesome and can be applied to a number of data related activities. Recycling chunks of code has proven useful to me.

RR

Aug 20, 2020

A very important course that greatly improved my ability to communicate the findings of any sort of data analysis that I perform. This is a critical skill to acquire to "deliver the means."

Фильтр по:

526–544 из 544 отзывов о курсе Воспроизводимое исследование

автор: Gianluca M

Oct 18, 2016

It is not a bad course, but it is very little informative. There is some nice general discussions about data science by the teacher, there is the explanation of the package knitr, and little else.

As part of the data science specialization it is nice. As a stand-alone course, I would definitely not recommend it.

автор: Julien N

Jul 23, 2018

Very disappointed by this course (was used to better by JHU)!

Nothing more than a R Markedown tutorial

Not up-to-date (a full video about an deprecated R package).

A section about evidence based analysis that is hard to understand (and of questionnable interest if not to "fill" this rather empty course)

автор: Roberto M

Nov 19, 2016

This course seems 'light' in content - too much time is spent reviewing case studies instead of discussing different ways to create documents that enable reproducible research. Perhaps this should be a topic/chapter in another course, and not a standalone course.

автор: Marvin T O

Mar 29, 2017

Reproducible research with doubt is important but videos and what it is discuss are not appealing and beyond that, what are worthen are the projects. I did not learn so much from the videos but by myself. Though, the forum is very useful.

автор: Matt E

May 01, 2018

This section could have been completed in a two week schedule instead of four. It is not a terribly complex subject. Statistical inference, however, is. It has a lot of content and could easily go for 5 or 6.

автор: Jackson L

Nov 08, 2017

This leaves a lot to be desired. I felt the lectures were fragmentary at best and really lacked in depth analysis. A lot of time was spent on the philosophy of analysis rather than practical tools in R.

автор: Willie C

Feb 03, 2020

Lecture videos were very repetitive. Course projects were repetitive, too. Important information, but didn't need to be stretched out over a full "four-week" course.

автор: Abhimanyu B

Jan 17, 2017

Provides a very summary overview of a very important aspect of data analysis. Expected more!

автор: Johnny C

Apr 03, 2018

The course was interesting, but it is bad many of the videos are recorded lectures.

автор: Pratik P

Feb 02, 2017

Sholdnt be a different course. It shold be very very concise. Not this long.

автор: Victor M

Dec 08, 2017

Last two weeks do not teach anything new

автор: Cyriana R

Jul 01, 2017

ok, but the focus is too much on knitr,

автор: Sindre F

Aug 01, 2016

Useful for academics.

автор: Avolyn F

Jun 19, 2019

I was really passionate about the subject matter, but, although I have experience in R, apparently not enough to complete the assignment. Would have liked a little more warning that this would be needed, I was more interested in the topic of Reproducible Research, which while I agree is easier done via code of some kind, shouldn't be a topic specific to R, should be applicable to Python, SQL, whatever.

Might have time to revisit this, but will probably need to take a few more R classes to even be able to complete, likely won't get around to it, but the first 2 weeks were worth the cost of paying for a certificate, I guess.

автор: Joel K

Feb 01, 2016

The other modules that I have done in this specialisation have been great. The lecturers are insightful and the courses have been at the right pace. This particular module was flat, to say the least. I paid €43 to learn a small amount of markdown syntax, and the quizzes and the weeks didn't even match up!

автор: matthieu c

Jun 10, 2017

The course presented an important topic, but it was not new to me. Moreover I believe that the quality of some audio track is not good enough to understand everything the lecturer is explaining. I'm referring to Roger Peng lecture with the students.

автор: Stefan H

Jul 01, 2019

Very repetitive in context of earlier introduction to the topic and also throughout the weeks. Generally it doesn't feel there is much of a take-away and not sure it deserves its own course.

автор: YAN N W T

Oct 11, 2017

Not much to take in this course comparing to the previous courses. Worst of all video lectures are not well organised.

автор: Anand M

May 05, 2017

Too much repetition; one video has been stretched into 10.