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

4.6
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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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176 - 200 de 547 revisiones para Reproducible Research

por Diana S

11 de feb. de 2016

Thank you son much!!!!

I really like the course.

It help me in my job =)

por Fikir W E

25 de feb. de 2020

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

por 易灿

2 de feb. de 2016

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

por Kevin H

9 de nov. de 2016

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

por Chong C F

20 de mar. de 2017

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

por Zhuang W

7 de nov. de 2017

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

por Leopoldo S

30 de oct. de 2016

Impressed. Great, great, course.

Enjoy and learn at the same time.

por Nurul H A

13 de sep. de 2020

Very good topic with the very good and challenging assessments.

por Fabio R C

24 de jul. de 2017

Great opportunity to become more scientific report the job in R

por carlos j m

11 de abr. de 2019

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

por Alzum S M

8 de ene. de 2019

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

por Brett W

4 de dic. de 2017

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

por Dorian P

8 de may. de 2017

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

por Carl W

9 de jul. de 2018

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

por Varishu P

3 de jul. de 2018

most nicely designed course in the specialization loved it

por Andrew

7 de abr. de 2019

One of my favorite courses in the specialization so far.

por Andreas K

12 de dic. de 2016

best course so far in the data scienist course package!

por James W

31 de oct. de 2016

This course helped me very much with my current career.

por Md G M

30 de jul. de 2018

Course contents are very good and easy to understands.

por Massimo M

15 de feb. de 2018

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

por Giovanni M C V

16 de feb. de 2016

Excellent course with great didactic. Congratulations!

por Chanpreet K

30 de dic. de 2018

Good course content. All things explained quite well.

por Dewald O

31 de oct. de 2018

Such a great course! The instructors are really good.

por César A

16 de jun. de 2020

Very nice program and a lot of practical exercices

por Mohammad A

20 de jul. de 2018

Great course , very informative and well organized