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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:

101–125 из 567 отзывов о курсе Воспроизводимое исследование

автор: Luz M S G

6 окт. 2020 г.

It was a good experience. The final project has been the most challenging that I have had in the specialization, but I learned a lot.

автор: Arjun S

27 авг. 2017 г.

Great stuff. Glad to have the course make us create an Rpubs profile and publish research. Recommended strongly for data scientists

автор: Daniel C J

14 нояб. 2016 г.

Great course. A must for every analyst for its simple tips on reproducibility, which can go a very very long way at work or school

автор: Omar N

8 нояб. 2018 г.

Really good module/course, gives you a glimpse into real world implementation of data science and the challenges involved with it.

автор: Tom B

19 янв. 2020 г.

Very practical and knowledge learned can be applied into my works as auditors. This can benefit any fields involving using data.

автор: Donald J

22 янв. 2018 г.

These are important skills for a data scientist and I'm glad there is a full 4-week course dedicated to reproducible research.

автор: Richmond S

29 сент. 2016 г.

I struggled in getting the final project right but it helped me understand the course better. Thumbs up reproducible research

автор: PRAKASH K

13 июля 2020 г.

I strongly recommend this course ,it focuses on reproducible research which is equally an important aspect of data analysis.

автор: Glenn W

4 мар. 2019 г.

Favorite course so far. Really enjoyed working on the projects. They were very helpful in helping to reinforce the material.

автор: Mathew E

30 мар. 2021 г.

This course has been an eye-opener for me and going forward, it would be an indispensable tool in my research activities.

автор: Amanyiraho R

13 янв. 2020 г.

Very interesting and tackles a very important issue that Data scientists always miss-out, reproducibility of your project

автор: Azat G

24 янв. 2019 г.

Amazing course, it introduced the concepts of reproducibility which is used to provide scientific fairness, transparency.

автор: Anusha V

3 янв. 2019 г.

Excellent Course - particular useful for anyone doing research and performing any kind of analysis on the observed data.

автор: Adrielle d C S

3 апр. 2016 г.

Muito completo. Inglês claro. Muitos exemplos. Chega a ser repetitivo em algumas aulas mas, antes sobrar do que faltar!

автор: Krishna B

30 мая 2017 г.

towards the end of week 1 lectures we can see all the parts of this specialization coming together in a very nice way!

автор: Monica Z

11 дек. 2020 г.

Very challenging. However, every step in this specialization improves my knowledge and the way of solving problems.

автор: Prem S

2 авг. 2017 г.

Nice course,especially it gives you a general idea and foundation on r markdown files if you already know R studio.

автор: Federico A V R

27 июля 2017 г.

This topic is relevant to the field, yet few institutions offer courses on it. Great knowledge, highly recommended.

автор: Lee Y L R

1 февр. 2018 г.

Clear sharing of the importance of having proper documentation of data analysis process to enable reproducibility.

автор: Ann B

14 мар. 2017 г.

I think this topic is sometimes overlooked, but very necessary. This course did a good job of covering the topic.

автор: Emily S

17 мая 2016 г.

I think this is an essential course that more people should take. Reproducibility is a huge issue in many fields.

автор: Courtney R

7 окт. 2019 г.

I really appreciated the topics covered in this course. Is a wonderful follow-up to the Exploratory Data course.

автор: Thiago M

12 авг. 2019 г.

course material and projects help a lot in learning and tips on how to better document research and projects

автор: Gregorio A A P

26 авг. 2017 г.

Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

автор: César A C

5 июня 2017 г.

I really needed this course to fully understand how to work with R from the raw data to publication. Nice ¡¡