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

4,113 calificaciones

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


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.


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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401 - 425 de 580 revisiones para Investigación reproducible

por Erik A

17 de ene. de 2017

The concept is very important, so it's good that this course is available. The video's are sometimes not that great of quality. It's okay to show recordings of lectures, but the sound is of less quality.

Another thing is that the teacher says a lot of "euhm". I know he cannot help that, but once you notice this it becomes a bit annoying.

por Charles K

31 de jul. de 2016

Pretty good course that introduces a lot of useful tools and the concept of reproduciblity. However, it is not quite as applicable as the previous courses for those who are individual contributors in the private sector and rarely have others double check their analyses or need to publish anything.

por Felipe P

29 de feb. de 2016

In this course, there was a slide presentation with audio recorded in a classroom. This part of the course should be replaced as soon as possible to offer better experience. As it is presented right now, with a loudy environment, it really doesn't match to the quality of the other courses.

por Werner S

28 de nov. de 2016

Besides the "real" scientists like medicine or bioscience, I think the whole community would be better off it everybody would follow the principles laid out here: Do your analysis but make sure that others can follow the rationale and that your steps are documented and thus reproducible.

por Jose P M L

23 de oct. de 2020

This is a very useful course, it shows how to search your research in a complete manner. This is very important and even though its an easier course, the idea is important. The audio in the lessons that are taken from professor Peng real classrom is deficient. Its hard to keep track

por Bernie P

19 de jun. de 2018

Good theory course. As someone who holds a masters and worked through half of a PhD it wasn't super useful for me personally since I was aware of the power and need for reproducibility. It's not worth while but having more of a business use case for the need might be helpful.

por Gabriel O M

21 de jun. de 2021

very nice, I learned new stuff that I didn't know. Very easy to follow and to understand as well. The exercises and projects are really good to practice previous knowledge acquired. Also I'm pretty sure that this course will help me out in my tasks at my current work.

por Camilla H

27 de sep. de 2017

The course finally got me to use markdown files which I had dabbled with before. It was nice to cement some knowledge. What I didn't appreciate was the largely redundant video lectures. Some were what seemed to be the same lecture given a year or two apart.

por Kyle H

5 de feb. de 2018

A few of the lectures were a bit repetitive if you are taking the full data science specialization. Overall there are some valuable skills and thought patterns that will prove useful if interested in reproducibility and clarity of analysis.

por Mengyin B

6 de oct. de 2016

It is about how to make your work available for others and yourself in the future. It is quite refreshing because I have never heard about anything in this course from anywhere else. It is useful for me and hope it will be useful to you.

por Yudhanjaya W

1 de sep. de 2017

The lessons on Knitr, Markdown and the case studies dissecting research were useful, but I felt far too little time was spent on examples of implementing reproducible research, and too much time spent talking about its benefits.

por Don M

10 de dic. de 2018

Good, but the final project involved too much programming and the size of the data file was unmanageable on my three year old laptop. Could the objectives be met with a smaller data file and less programming?

por Yevgen M

6 de abr. de 2017

If you are at university (PhD student, academic, researcher, etc.) then you kind of know most of the "theory". However, practising R was a huge plus (personally, I liked the Week 4 task).

por Yatin M

22 de jul. de 2017

Learning Knitr was cool. However, many of the slides were not directly relevant to the course. I think, more rigor can be added, or this course can be merged with one of the others.

por Giovanna A G

16 de dic. de 2016

You will learn how to use a very valuable tool in this class; its name is R Markdown. Besides Prof. Peng explains very well the importance of reproducible research. Nice course!

por Kim K

8 de ago. de 2018

Very helpful and informative information on how to create reproducible research. The project gives you an opportunity to create reproducible research in the format of a report.

por Antonio C d S P

3 de feb. de 2017

While I'm pretty sure this course is VERY important for researchers, it is not very useful for my area (IT) and I would like to know this before taking the course. Thank you.

por Greg A

22 de feb. de 2018

This is a necessary evil. You can try to do the other classes in the specialization without it, but learning to use R markdown well is hard with out this or a similar class

por Manny R

13 de nov. de 2017

Enjoyed learning about rMarkdown, caching, and RPubs. Was also able to spend time plotting and aggregating data in different ways. Didn't enjoy cleaning data too much :)

por demehin I

23 de may. de 2016

it shows how to better communicate one analysis and i have learnt a lot from it. the lectures should be updated as some details and figures were irrelevant a this time

por Mikhail S

6 de feb. de 2016

First week has an assignment that requires knowledge from the second week. It would be better for the course if both assignments has two weeks for accomplishment.

por Jorge E M O

21 de jul. de 2016

The course already needs and actualization, plus they must fix the order of the first assignment. Besides that, this is a really useful and fulfilling course.

por Jo S

27 de ene. de 2016

Covers some important and interesting areas and is generally well taught (although the recording quality on the videos varies). Interesting final project!

por Rouholamin R

12 de may. de 2019

lectures are a little bit theoretical and at some point maybe boring but projects will give you a real experience with data and research reproducibility.

por Kaplanis A

26 de dic. de 2016

All in all a great course with very valuable information to make a data scientist better at his job. However it could have been covered in 2 weeks time