Chevron Left
Вернуться к Воспроизводимое исследование

Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

Оценки: 4,065
Рецензии: 584

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

12 февр. 2016 г.

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.

19 авг. 2020 г.

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:

551–567 из 567 отзывов о курсе Воспроизводимое исследование

автор: Marvin T O

29 мар. 2017 г.

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

1 мая 2018 г.

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

8 нояб. 2017 г.

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

2 февр. 2020 г.

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

17 янв. 2017 г.

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

автор: Johnny C

3 апр. 2018 г.

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

автор: Pratik P

2 февр. 2017 г.

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

автор: Victor M

8 дек. 2017 г.

Last two weeks do not teach anything new

автор: Cyriana R

1 июля 2017 г.

ok, but the focus is too much on knitr,

автор: Sindre F

1 авг. 2016 г.

Useful for academics.

автор: Avolyn F

19 июня 2019 г.

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: Owen D

8 сент. 2021 г.

This course could've been condensed into one video and incorporated somewhere else in the specialization. Doesn't seem like the instructor even took the course that seriously despite emphasizing its importance in the first lecture. Some of the lectures sound like they were recorded while the instructor was drinking with his colleagues at a dinner party.

автор: Joel K

1 февр. 2016 г.

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

10 июня 2017 г.

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

1 июля 2019 г.

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

11 окт. 2017 г.

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

автор: Anand M

5 мая 2017 г.

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