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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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3,947 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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526 - 547 de 547 revisiones para Reproducible Research

por Anton K

27 de nov. de 2019

The material is shallow. Projects are way too time demanding. Everybody knows that data cleaning is a routine and long process. That is precisely why nobody likes it. And if there's only one way to clean the data - by hand and only after reading a lot of related database documentation - this kills all the fun of studying and makes the overall picture of consepts relations unclear.

por Jaymes P

27 de oct. de 2020

This course was not well organized. It seemed like lessons were just thrown together and covered much the same content. Some were recorded lectures, others filmed in an office, saying virtually the same things with nearly the same slides but on different weeks. Same old story with the instructor--impossible to listen to because of all the so, um, uhhh, so, kinda, so ummm, etc.

por Gonzalo A D

5 de mar. de 2016

The course is poorly organised: There is a project on week one that requires knowledges of week two. Some concepts are dictated more than once because it uses videos made for this course + other recorded from a class room.

I think this course should be a 3 weeks project and the price should be the half of it cost.

Though I enjoyed the second project.

por Gianluca M

18 de oct. de 2016

It is not a bad course, but it is very little informative. There is some nice general discussions about data science by the teacher, there is the explanation of the package knitr, and little else.

As part of the data science specialization it is nice. As a stand-alone course, I would definitely not recommend it.

por Julien N

23 de jul. de 2018

Very disappointed by this course (was used to better by JHU)!

Nothing more than a R Markedown tutorial

Not up-to-date (a full video about an deprecated R package).

A section about evidence based analysis that is hard to understand (and of questionnable interest if not to "fill" this rather empty course)

por Roberto M

19 de nov. de 2016

This course seems 'light' in content - too much time is spent reviewing case studies instead of discussing different ways to create documents that enable reproducible research. Perhaps this should be a topic/chapter in another course, and not a standalone course.

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 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.