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

4.6
estrellas
4,088 calificaciones
591 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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476 - 500 de 573 revisiones para Investigación reproducible

por João R

3 de jun. de 2017

Too repetitive. Could be shorter.

por ric j n

7 de abr. de 2017

The course is very informative.

por Iair M L S J

18 de ene. de 2017

Could have more content in deep

por Naman D D

21 de jun. de 2020

It was an informative course.

por SAKINA Z

8 de jul. de 2020

great learning experience!!

por Sathiaseelan P

7 de jun. de 2018

This Course was really fun.

por Mark S

9 de nov. de 2017

Import information to know.

por Mohamad R B R

18 de feb. de 2016

Still i​ can used easily.

por Timothy V B

19 de may. de 2017

Good intro to concepts

por Vadim K

26 de may. de 2016

Rather general topic

por Sourav B

12 de jul. de 2017

informative course

por Jason W

3 de ene. de 2017

latex is better :O

por Amit S

26 de nov. de 2019

Very Good Content

por Thiago Y

27 de jun. de 2021

Very cool course

por Rohit K S

21 de sep. de 2020

AmazingCourse!!

por Mehul P

17 de sep. de 2017

Nice course.

por Abhishek S

6 de jun. de 2017

Good course

por Tushar K

10 de feb. de 2017

Nice course

por Johnnery A

22 de nov. de 2019

Excellent!

por Khobindra N C

18 de may. de 2016

Excellent

por Anup K M

2 de oct. de 2018

good

por Greg B G

21 de sep. de 2017

nice

por Rajib K

28 de mar. de 2017

u

por Miguel C

6 de abr. de 2020

I enjoyed this course, especially the tips we got on how to make our analysis more reproducible and the practice with RMarkdown and RPubs. The lecturer was really knowledgeable and engaging, making it easier to follow the course. The assignments were challenging and allowed me to build on things I had learned in previous courses, especially R skills.

My biggest problem with this course was its repetitiveness. Its content repeats some of what was explained in Data Scientist's Toolbox course, and sometimes it repeated things from previous week of this same course. I think the material can be summarized into just 2 or 3 weeks. I also found the second course project quite exhaustive; it wasn't particularly hard but the data was quite messy so it took a long time to clean, which was boring and tiring but I guess that's part of the data scientist's job.

Overall, I still enjoyed the course and I would recommend it to other people interested in becoming data scientists.

por Christiane H

8 de dic. de 2015

Overall a good course for self-study. The assignments in particular are excellent for data cleaning, analysis and interpretation. The quizzes are very basic though and appear to be there only to check if the student has gone through the lectures. The knowledge needed to answer the quizzes and achieve the desired results in the assignments are vastly different and should be addressed.

The case studies at the end are insightful and more use could be made of them in a more advanced course. There is a lot of repetition of concepts throughout the course and this can become distracting. THe format for the lecture videos varies throughout and this inconsistency (along with extreme audio volume changes) also becomes distracting.

Other than that, excellent for driving the need for reproducible research (RR) home, presenting and explaining some tools available to achieve RR and ways of publishing results/reports from these studies.