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

4.6
estrellas
4,104 calificaciones
597 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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426 - 450 de 579 revisiones para Investigación reproducible

por Brett A

24 de abr. de 2016

Overall I found this course useful. My only complaint is that the material needed to complete the first assignment in week 1 came in week 2.

por Alex F

17 de ene. de 2018

Good principles, lectures are improving but still a bit dry and very boring slides. I learned more from my peer reviews than anything else.

por BIBHUTI B P

30 de may. de 2017

Good explication of reproducible analysis and representation of didactic approached towards it.

Thank you & keep up the tutoring skills...

por Patrick S

9 de feb. de 2017

Good course as part of the data science specialization. Much effort needed for assignments in contrast to this relative light topic.

por Robert M

12 de dic. de 2016

Very good course. Would love to get to see examples of some advanced usage of knitr in developing presentations and complex reports.

por Chris R

4 de ene. de 2022

Great course. Some of the materials are now a bit dated, but I really appreciated the content and the projects to skill-build.

por Naeem B

22 de jun. de 2018

At first this course seems boring but have realized importance after seeing bio statistic prescription drug video of week 4.

por LIWANGZHI

19 de dic. de 2018

This course provides me with some new ideas about reproducible research and allows me to learn how to wrie .Rmd files.

por Tim S

30 de may. de 2016

This was another very useful course in the series, with (peer reviewed) assignments taking on a very significant role.

por Minki J

1 de ene. de 2018

peer assignment is tough, hard and great to learn.

but the course is very general, not that related to the assignment

por Igor T

26 de feb. de 2017

Good course. Especially enjoyed final course project. It's really challenging and looks like a real‑life task.

por Mehrdad P

26 de sep. de 2019

Course nicely highlighted the importance of reproducible research and the use of markdown and knitr packages.

por Sawyer W

1 de ago. de 2017

Good course. Nice overview of concepts of reproduciblity and tools for doing so (sweave, knitr, RPubs)

por Jason C

6 de may. de 2016

Very good, but maybe not at solid as those before it. Some reproducibility concepts felt a bit vague.

por Nicolás H

13 de sep. de 2020

Necesario para conocer, emplear buenas prácticas y darle validez científica a los trabajos realizados

por Asif K

17 de sep. de 2018

Very good content and pace. Got good hands on experience, right content and structure of assignments

por Brian F

8 de jul. de 2017

Although there is not a lot to this course I like that it covers an area that is often neglected.

por Jeremy J

11 de sep. de 2016

Some of the material seems pretty rote but it did introduce some new software and capabilities.

por Luiz E B J

21 de oct. de 2019

This is a good course tht open our minds and eyes to the relevance of Reproducible Research.

por Joseph F

13 de ene. de 2021

Now I appreciate what is the importance of a reproducible research! Awesome course overall!

por Shivangi P

3 de ago. de 2020

It is a nicely structured course with introduction to R and gives a brief of data science.

por Francisco M R O

9 de mar. de 2019

It was very useful for me, now I know the importance of making data analysis reproducible.

por Korwin A

6 de feb. de 2016

Great class with excellent supporting material. A little chaotic, but very good overall.

por Amol M

18 de may. de 2020

This course provides an easy way out to create reports which can be shared with others.

por Thej K

12 de mar. de 2019

Nothing serious in this course! Rmd is a good tool to work with! and get familiar with!