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Opiniones y comentarios de aprendices correspondientes a Reproducible Research por parte de Universidad Johns Hopkins

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3,951 calificaciones
564 reseña

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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....

Principales reseñas

AA
12 de feb. de 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
19 de ago. de 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."

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101 - 125 de 547 revisiones para Reproducible Research

por Daniel C J

14 de nov. de 2016

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

por Omar N

8 de nov. de 2018

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

por ONG P S

19 de ene. de 2020

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

por Donald J

22 de ene. de 2018

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

por Richmond S

29 de sep. de 2016

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

por PRAKASH K

13 de jul. de 2020

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

por Glenn W

4 de mar. de 2019

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

por Amanyiraho R

13 de ene. de 2020

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

por Azat G

24 de ene. de 2019

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

por Anusha V

3 de ene. de 2019

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

por Adrielle S

3 de abr. de 2016

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

por Krishna B

30 de may. de 2017

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

por Prem S

2 de ago. de 2017

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

por Federico A V R

27 de jul. de 2017

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

por Lee Y L R

1 de feb. de 2018

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

por Ann B

14 de mar. de 2017

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

por Emily S

17 de may. de 2016

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

por Courtney R

7 de oct. de 2019

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

por Thiago

12 de ago. de 2019

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

por Gregorio A A P

26 de ago. de 2017

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

por César A C

5 de jun. de 2017

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

por Jared P

10 de abr. de 2016

Loved it. The concepts around reproducible research are important. Should be mandatory teaching in school.

por Suryadipta D

12 de abr. de 2018

well organized and easy-to-understand subject material, shapes up really well towards the specialization.

por Marco C

25 de feb. de 2018

Very useful course to build a scientific way of thinking, and publishing my work has been very engaging.

por santiago R

29 de nov. de 2017

Very nice course. R Markdown make everything looks better and understandable for a reproducible research.