Chevron Left
Вернуться к Воспроизводимое исследование

Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

4.6
звезд
Оценки: 4,047
Рецензии: 579

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
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.

RR
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:

401–425 из 561 отзывов о курсе Воспроизводимое исследование

автор: Giovanna A G

16 дек. 2016 г.

You will learn how to use a very valuable tool in this class; its name is R Markdown. Besides Prof. Peng explains very well the importance of reproducible research. Nice course!

автор: Kim K

8 авг. 2018 г.

Very helpful and informative information on how to create reproducible research. The project gives you an opportunity to create reproducible research in the format of a report.

автор: Antonio C d S P

3 февр. 2017 г.

While I'm pretty sure this course is VERY important for researchers, it is not very useful for my area (IT) and I would like to know this before taking the course. Thank you.

автор: Greg A

22 февр. 2018 г.

This is a necessary evil. You can try to do the other classes in the specialization without it, but learning to use R markdown well is hard with out this or a similar class

автор: Manny R

13 нояб. 2017 г.

Enjoyed learning about rMarkdown, caching, and RPubs. Was also able to spend time plotting and aggregating data in different ways. Didn't enjoy cleaning data too much :)

автор: demehin I

23 мая 2016 г.

it shows how to better communicate one analysis and i have learnt a lot from it. the lectures should be updated as some details and figures were irrelevant a this time

автор: Mikhail S

6 февр. 2016 г.

First week has an assignment that requires knowledge from the second week. It would be better for the course if both assignments has two weeks for accomplishment.

автор: Jorge E M O

21 июля 2016 г.

The course already needs and actualization, plus they must fix the order of the first assignment. Besides that, this is a really useful and fulfilling course.

автор: Jo S

27 янв. 2016 г.

Covers some important and interesting areas and is generally well taught (although the recording quality on the videos varies). Interesting final project!

автор: Rouholamin R

12 мая 2019 г.

lectures are a little bit theoretical and at some point maybe boring but projects will give you a real experience with data and research reproducibility.

автор: Kaplanis A

26 дек. 2016 г.

All in all a great course with very valuable information to make a data scientist better at his job. However it could have been covered in 2 weeks time

автор: Luiz C

17 сент. 2017 г.

Interesting course, but course assginments lack guidance, have too much complexity and require a time spent too long compared to the benefits

автор: Brett A

24 апр. 2016 г.

Overall I found this course useful. My only complaint is that the material needed to complete the first assignment in week 1 came in week 2.

автор: Alex F

17 янв. 2018 г.

Good principles, lectures are improving but still a bit dry and very boring slides. I learned more from my peer reviews than anything else.

автор: BIBHUTI B P

30 мая 2017 г.

Good explication of reproducible analysis and representation of didactic approached towards it.

Thank you & keep up the tutoring skills...

автор: Patrick S

9 февр. 2017 г.

Good course as part of the data science specialization. Much effort needed for assignments in contrast to this relative light topic.

автор: Robert M

12 дек. 2016 г.

Very good course. Would love to get to see examples of some advanced usage of knitr in developing presentations and complex reports.

автор: Naeem B

22 июня 2018 г.

At first this course seems boring but have realized importance after seeing bio statistic prescription drug video of week 4.

автор: LIWANGZHI

19 дек. 2018 г.

This course provides me with some new ideas about reproducible research and allows me to learn how to wrie .Rmd files.

автор: Tim S

30 мая 2016 г.

This was another very useful course in the series, with (peer reviewed) assignments taking on a very significant role.

автор: Minki J

1 янв. 2018 г.

peer assignment is tough, hard and great to learn.

but the course is very general, not that related to the assignment

автор: Igor T

26 февр. 2017 г.

Good course. Especially enjoyed final course project. It's really challenging and looks like a real‑life task.

автор: Mehrdad P

26 сент. 2019 г.

Course nicely highlighted the importance of reproducible research and the use of markdown and knitr packages.

автор: Sawyer W

1 авг. 2017 г.

Good course. Nice overview of concepts of reproduciblity and tools for doing so (sweave, knitr, RPubs)

автор: Jason C

6 мая 2016 г.

Very good, but maybe not at solid as those before it. Some reproducibility concepts felt a bit vague.