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Opiniones y comentarios de aprendices correspondientes a Investigación reproducible por parte de Universidad Johns Hopkins

4.6
estrellas
4,065 calificaciones
584 reseña

Acerca del Curso

Este curso se centra en los conceptos y las herramientas que permiten realizar análisis de datos modernos de forma reproducible. La investigación reproducible se basa en la idea de que los análisis de datos y, en general, las afirmaciones científicas, se publican con sus datos y el código del software para que otros puedan verificar los hallazgos y basarse en ellos. La necesidad de reproducibilidad aumenta drásticamente a medida que los análisis de datos se vuelven más complejos, con conjuntos de datos más grandes y cálculos más sofisticados. La reproducibilidad permite que las personas se centren en el contenido real de un análisis de datos, en lugar de en los detalles superficiales que aparecen en un resumen escrito. Además, la reproducibilidad hace que un análisis sea más útil para otros, ya que los datos y el código que en realidad permitieron llevar a cabo el análisis están disponibles. Este curso se centrará en las herramientas de análisis estadístico alfabetizadas que permiten publicar los análisis de datos en un único documento que permite a otros ejecutar fácilmente el mismo análisis para obtener los mismos resultados....

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 567 revisiones para Investigación reproducible

por Luz M S G

6 de oct. de 2020

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.

por Arjun S

27 de ago. de 2017

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

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 Tom B

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 Mathew E

30 de mar. de 2021

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

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 d C 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 Monica Z

11 de dic. de 2020

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

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 M

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 ¡¡