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Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

4.6
звезд
Оценки: 3,955
Рецензии: 564

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:

176–200 из 547 отзывов о курсе Воспроизводимое исследование

автор: Diana S

10 февр. 2016 г.

Thank you son much!!!!

I really like the course.

It help me in my job =)

автор: Fikir W E

25 февр. 2020 г.

I am thankful that such a quality learning material is made available.

автор: 易灿

2 февр. 2016 г.

Very helpful, let me know new tools like knitr and Rmarkdown language!

автор: Kevin H

9 нояб. 2016 г.

Coding documents and data cleaning is possibly the best thing ever =D

автор: Chong C F

20 мар. 2017 г.

Everyone should know this, every thing should have prove and balance

автор: Zhuang W

7 нояб. 2017 г.

Great course! Help us to build the basic skills in data analysis.

автор: Leopoldo S

30 окт. 2016 г.

Impressed. Great, great, course.

Enjoy and learn at the same time.

автор: Nurul H A

13 сент. 2020 г.

Very good topic with the very good and challenging assessments.

автор: Fábio R C

24 июля 2017 г.

Great opportunity to become more scientific report the job in R

автор: carlos j m

11 апр. 2019 г.

Great course, good lectures. I learned a lot of usable skills.

автор: Alzum S M

8 янв. 2019 г.

A great course that will take you ahead to be a Data Scientist

автор: Brett W

4 дек. 2017 г.

I really liked this course. I have carried a lot out of it.

автор: Dorian P

8 мая 2017 г.

Very nice course, learn a lot with it. Thank you very much.

автор: Carl W

9 июля 2018 г.

Knitr was a nice tool to learn. I can see it being useful.

автор: Varishu P

3 июля 2018 г.

most nicely designed course in the specialization loved it

автор: Andrew

7 апр. 2019 г.

One of my favorite courses in the specialization so far.

автор: Andreas K

12 дек. 2016 г.

best course so far in the data scienist course package!

автор: James W

31 окт. 2016 г.

This course helped me very much with my current career.

автор: Md G M

30 июля 2018 г.

Course contents are very good and easy to understands.

автор: Massimo M

15 февр. 2018 г.

Very nice course, easy to follow and very well taught.

автор: Giovanni M C V

16 февр. 2016 г.

Excellent course with great didactic. Congratulations!

автор: Chanpreet K

30 дек. 2018 г.

Good course content. All things explained quite well.

автор: Dewald O

31 окт. 2018 г.

Such a great course! The instructors are really good.

автор: César A

16 июня 2020 г.

Very nice program and a lot of practical exercices

автор: Mohammad A

20 июля 2018 г.

Great course , very informative and well organized