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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

3,852 个评分
550 条评论


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 13, 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 20, 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."


476 - 可重复性研究 的 500 个评论(共 534 个)

创建者 Michalis F

May 21, 2016

Too expensive for the material it provides; it is helpful and necessary but this course can be summarised in 1-2 lectures. There is a very good lecture from an external speaker,which was very good and funny (at least i found it funny) and i didn't realise that it was 30 mins long.

创建者 Fabiana G

Jun 23, 2016

Course content is okay - there was some repetition of topics throughout the weeks. As the other first courses in the specialization, students would benefit tremendously if the instructors were a bit more active - the course feels out of date and abandoned.

创建者 Rafael S

Oct 27, 2017

This was, by far, the hardest course in the specialization until now. Not because of its dificulty per se, but because it was too boring, there where very little practical exercises, and I just had to gather all my willpower to get to the end of this one.

创建者 Moshe P

May 22, 2019

The course seems to be based on lectures recorded at different times. Some points discussed are repetitive. the quality of content is good though. I believe the whole material may have to be updated and, potentially, re-recorded.

创建者 Ekta A

Feb 23, 2018

Most of the knowledge one needs can be perceived till week 2 only. Week 3 is a complete repetition of previous 2 weeks. While week 4 offers case studies which I feel are not much important. But overall the experience was good.

创建者 Rashaad J

Oct 03, 2017

This is a good course for people who don't have experience with conducting research. For experienced researchers, the content provided is not too informative. More discussions on R Markdown should have been provided.

创建者 Hua-Poo S

Feb 19, 2017

I had difficultly with the two assignments, not because they were difficult but because the instructions were not clear. From reviewing other's assignments, it did not appear to be just me.

创建者 Tony W

Jul 16, 2016

Has interesting ideas and approach to forming a structure way of analysing a problem. The module does feel a little thin in content, and perhaps should be combined with Exploratory Analysis.


Dec 13, 2017

A bit too much focused on academic research, I find. Quality of the video's isn't always top-notch either.

Good exercises to practice plotting skills with interesting, real-life data sets.

创建者 Brittany S

Nov 01, 2018

I wish they'd stop labeling the course projects as two hours. The week 4 project took a lot longer than that (closer to a week). Also, a lot of the information presented was repetitive.

创建者 Rose G

Mar 31, 2020

Good introduction to Rpubs, and important remainder of the importance of reproducible research for scientists, but it may be a bit too much to focus an entire course only on that.

创建者 Michał M

Jan 28, 2016

Some of the videos has low quality, which make them harder to understand for non native speakers. In my opinion there is also too less tips for second assessment.

创建者 Oña G L E

Aug 23, 2018

The videos doesn't listen well, and some activities are not interesting, you could teach swave and some of latex instead of repeat some parts of other courses.


Sep 01, 2017

This course has contents that are repeated multiple times throughout the course. I think entire course could have been covered in a week or at most two weeks.

创建者 Joseph C

Feb 08, 2016

The first week assignment should really be the second week assignment since all the lessons about knitr would have made the assignment much easier.

创建者 Andreas S J

Oct 04, 2017

Important and interesting stuff - but lots of it is repeated too much, which make it seem like 4 weeks is too much for the material.

创建者 Fabiano G d S

Mar 07, 2016

It's, for sure, a necessary content but don't feel like something that deserves to be on this specialization. Content is good.

创建者 James O

Oct 31, 2016

Interesting material, but wasn't necessarily of the same depth of knowledge like previous courses in the series

创建者 Diego T B

Nov 17, 2017

This topic is very interesting. But I think that was very large and without as practical things in videos.

创建者 Robert K

Jun 12, 2017

This information is useful, but it felt like this could have been condensed in to a couple of weeks.

创建者 Raushon K

Feb 18, 2016

Week1 can be explained better. First assignment i was clueleass on Kintr and how to generate report.

创建者 Nathan M

Jun 11, 2016

Why is this its own class? Seems like it could have been covered in a week somewhere else.

创建者 Rohit S A

Oct 20, 2016

Not a well structured course. Also, not very motivating to go through this one.

创建者 Fernando M

Sep 04, 2017

Don´t like this topics but I understand that they are necessary. Course is ok

创建者 Corbin C

Apr 23, 2018

Good material, but some of it is out of date (like deprecated functions).