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Avis et commentaires pour d'étudiants pour Recherche reproductible par Université Johns-Hopkins

4.6
étoiles
4,115 évaluations

À propos du cours

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....

Meilleurs avis

AA

12 févr. 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 août 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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201 - 225 sur 580 Avis pour Recherche reproductible

par Carl W

9 juil. 2018

Knitr was a nice tool to learn. I can see it being useful.

par V P

3 juil. 2018

most nicely designed course in the specialization loved it

par Andrew

7 avr. 2019

One of my favorite courses in the specialization so far.

par Andreas K

12 déc. 2016

best course so far in the data scienist course package!

par James W

31 oct. 2016

This course helped me very much with my current career.

par Md G M

30 juil. 2018

Course contents are very good and easy to understands.

par Massimo M

15 févr. 2018

Very nice course, easy to follow and very well taught.

par Giovanni M C V

16 févr. 2016

Excellent course with great didactic. Congratulations!

par Chanpreet K

30 déc. 2018

Good course content. All things explained quite well.

par Dewald O

31 oct. 2018

Such a great course! The instructors are really good.

par César A

16 juin 2020

Very nice program and a lot of practical exercices

par Mohammad A

20 juil. 2018

Great course , very informative and well organized

par Lei S

27 déc. 2017

Only thing: maybe some lectures should be updated.

par phani v k

7 janv. 2017

This is a very good course for a begineer like me.

par Laro N P

2 mai 2018

Good course. Every new course is a new challenge.

par Shivanand R K

21 juin 2016

Great and Excellent thoughts and course material.

par מיקי כ

18 août 2020

Great course. very important for any researcher.

par Trung N T

8 mai 2017

The course very good for beginner data scientist

par Damian S

16 nov. 2021

Interesting course with well prepared exercises

par ILLYA B

12 oct. 2020

The best course of John Hopkins Specialization!

par Akram N

2 mai 2019

Very fruitful. I enjoyed this lesson very much.

par Jamie M

26 oct. 2018

Good course. Does exactly what it says it does.

par Utku K

14 nov. 2016

Good lesson, about an interesting topic for me.

par Predrag M

13 mars 2016

One of the best courses in this specialization.

par Bipin K

10 févr. 2016

great one to know how about researches are done