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学生对 约翰霍普金斯大学 提供的 可重复性研究 的评价和反馈

4,138 个评分


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....



Feb 12, 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.


Aug 19, 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."


376 - 可重复性研究 的 400 个评论(共 582 个)

创建者 刘博

Mar 2, 2017

good work

创建者 Carlos R

Dec 26, 2016


创建者 saroj r

May 14, 2016

i like it

创建者 杜冈桃

Oct 7, 2017


创建者 Sanjay B

Oct 27, 2020


创建者 Medha B

Oct 18, 2020


创建者 Adán H

Nov 6, 2017


创建者 Zhao M

Nov 1, 2016


创建者 manoj k

Aug 31, 2016


创建者 Chandan K S

Nov 13, 2020



Jul 17, 2020


创建者 Rizwan M

Sep 5, 2019


创建者 SriHari a

Apr 21, 2019


创建者 Amit K R

Nov 27, 2017


创建者 Jay B

Aug 24, 2017


创建者 Yi-Yang L

Apr 10, 2017


创建者 Oleksandr F

Nov 24, 2016


创建者 朱荣荣

Mar 11, 2016


创建者 Meidani P

Dec 3, 2021


创建者 Suriya

Feb 24, 2018


创建者 Marat G

Mar 22, 2017


创建者 Jeffrey P

Mar 15, 2016

By far the most time consuming, yet rewarding course in the data science specialization thus far. Literate Programing in general and R Markdown in particular are simple enough as concepts, but do take some time to grow accustomed to. However, I found the course to be a compelling argument for reproducibility that has application beyond just Data Science proper.

Although the technology is completely different, the concepts behind reproducibility really resonated with me and the work I do managing a division in Application Development. I'm constantly having to balance seemingly limitless demands, limited resources, and the difficulty of retaining staff in highly-competitive industry. Reproducibility becomes not just the basis for cross-training, product stabilization, and growth, but is a necessary ingredient of a team's survival.

This course not only cemented my own thoughts on the topic, but gave me some new ideas and tools for process improvement on the job.

创建者 Nicolas L

Apr 15, 2020

El proyecto final del curso tiene poco que ver con lo enseñado a lo largo de éste, era muy necesario haber tomado los cursos anteriores (en especial R programming y Exploración de Datos). Además, el proyecto debería estar mejor planificado, se buscaba que la mayor parte del tiempo estuviera en limpiar la data? O un objetivo más fuerte era el uso de gráficos más elaborados u otro al interior de RMarkdown? O un análisis un poco más elaborado que sólo sumar?

创建者 Siying R

Oct 21, 2018

This course teaches how to present a R code analysis that others can run the code to reproduce the same result. The length of the lecture is minimum and the project helps me to make the reproducible analysis on my own. One thing I would like to see improvement is that the instructor's speech. I hope that he can speak more smoothly without stopping to repeat words. It was quite a struggle to listen to his talking. Thank you.

创建者 Travis M

Apr 2, 2016

The first assignment should occur during the second week instead of the first given how the material is presented. The second and final project is very time consuming. Ideally this course should run for 6 weeks instead of 4 because of this. The second project is challenging and it definitely drives home the point about reproducible result given the state of the raw data.