Chevron Left
Volver a Investigación reproducible

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."

Filtrar por:

551 - 567 de 567 revisiones para Investigación reproducible

por Marvin T O

29 de mar. de 2017

Reproducible research with doubt is important but videos and what it is discuss are not appealing and beyond that, what are worthen are the projects. I did not learn so much from the videos but by myself. Though, the forum is very useful.

por Matt E

1 de may. de 2018

This section could have been completed in a two week schedule instead of four. It is not a terribly complex subject. Statistical inference, however, is. It has a lot of content and could easily go for 5 or 6.

por Jackson L

8 de nov. de 2017

This leaves a lot to be desired. I felt the lectures were fragmentary at best and really lacked in depth analysis. A lot of time was spent on the philosophy of analysis rather than practical tools in R.

por Willie C

2 de feb. de 2020

Lecture videos were very repetitive. Course projects were repetitive, too. Important information, but didn't need to be stretched out over a full "four-week" course.

por Abhimanyu B

17 de ene. de 2017

Provides a very summary overview of a very important aspect of data analysis. Expected more!

por Johnny C

3 de abr. de 2018

The course was interesting, but it is bad many of the videos are recorded lectures.

por Pratik P

2 de feb. de 2017

Sholdnt be a different course. It shold be very very concise. Not this long.

por Victor M

8 de dic. de 2017

Last two weeks do not teach anything new

por Cyriana R

1 de jul. de 2017

ok, but the focus is too much on knitr,

por Sindre F

1 de ago. de 2016

Useful for academics.

por Avolyn F

19 de jun. de 2019

I was really passionate about the subject matter, but, although I have experience in R, apparently not enough to complete the assignment. Would have liked a little more warning that this would be needed, I was more interested in the topic of Reproducible Research, which while I agree is easier done via code of some kind, shouldn't be a topic specific to R, should be applicable to Python, SQL, whatever.

Might have time to revisit this, but will probably need to take a few more R classes to even be able to complete, likely won't get around to it, but the first 2 weeks were worth the cost of paying for a certificate, I guess.

por Owen D

8 de sep. de 2021

This course could've been condensed into one video and incorporated somewhere else in the specialization. Doesn't seem like the instructor even took the course that seriously despite emphasizing its importance in the first lecture. Some of the lectures sound like they were recorded while the instructor was drinking with his colleagues at a dinner party.

por Joel K

1 de feb. de 2016

The other modules that I have done in this specialisation have been great. The lecturers are insightful and the courses have been at the right pace. This particular module was flat, to say the least. I paid €43 to learn a small amount of markdown syntax, and the quizzes and the weeks didn't even match up!

por matthieu c

10 de jun. de 2017

The course presented an important topic, but it was not new to me. Moreover I believe that the quality of some audio track is not good enough to understand everything the lecturer is explaining. I'm referring to Roger Peng lecture with the students.

por Stefan H

1 de jul. de 2019

Very repetitive in context of earlier introduction to the topic and also throughout the weeks. Generally it doesn't feel there is much of a take-away and not sure it deserves its own course.

por YAN N W T

11 de oct. de 2017

Not much to take in this course comparing to the previous courses. Worst of all video lectures are not well organised.

por Anand M

5 de may. de 2017

Too much repetition; one video has been stretched into 10.