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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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176 - 200 sur 580 Avis pour Recherche reproductible

par Frederik C

29 mai 2018

Key aspect for a good data scientist. It was a nice introduction to knitr etc...

par Jose P

11 févr. 2018

Perfect to aid past and present curation and validation of research. Thank you!

par Emil L

3 nov. 2016

Great Course, should be free to all freshman graduate students across the world.

par Jim M

22 mai 2020

Very nice final course pulling everything together from the previous 4 courses.

par Nilrey J D C

7 oct. 2019

A very good course to know why it is important to have a reproducible research.

par xuwei. l

15 mai 2016

excellent course to introduce practical approach for reporting data analysis !

par Harland H

1 juil. 2018

Very informative. Will use this on the job to make producing products easier.

par Andrés D C

21 déc. 2020

Excellent proposal, one of the important things in the scientific research

par SATHYANARAYANAN S

10 sept. 2017

Very good for anyone wanting to get into the field of Data Science using R

par Anita M G

5 août 2020

Thank you So much !!!

This course wonderfull.. Learnt so many new things.

par Diana S

11 févr. 2016

Thank you son much!!!!

I really like the course.

It help me in my job =)

par Fikir W E

25 févr. 2020

I am thankful that such a quality learning material is made available.

par 易灿

2 févr. 2016

Very helpful, let me know new tools like knitr and Rmarkdown language!

par Kevin H

9 nov. 2016

Coding documents and data cleaning is possibly the best thing ever =D

par Chong C F

20 mars 2017

Everyone should know this, every thing should have prove and balance

par R. V

16 mai 2022

VERY CHALLENGING course! But it's all good! You will learn a lot.

par Zhuang W

7 nov. 2017

Great course! Help us to build the basic skills in data analysis.

par Leopoldo S

30 oct. 2016

Impressed. Great, great, course.

Enjoy and learn at the same time.

par Nurul H A

13 sept. 2020

Very good topic with the very good and challenging assessments.

par Fábio R C

24 juil. 2017

Great opportunity to become more scientific report the job in R

par carlos j m

11 avr. 2019

Great course, good lectures. I learned a lot of usable skills.

par Alzum S M

8 janv. 2019

A great course that will take you ahead to be a Data Scientist

par JAKOB L

26 avr. 2022

Really good course. Good introduction to an important topic.

par Brett W

4 déc. 2017

I really liked this course. I have carried a lot out of it.

par Dorian P

8 mai 2017

Very nice course, learn a lot with it. Thank you very much.