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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

4.5
3,183 个评分
455 个审阅

课程概述

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....

热门审阅

AA

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.

AS

Jun 23, 2017

Of course, I liked this course. There was even an extra non-graded assignment. Plus two graded assignments. Quality instruction videos and lots of practice. Everything a learner needs.

筛选依据:

1 - 可重复性研究 的 25 个评论(共 443 个)

创建者 Dzmitry S

May 10, 2016

Too expensive for such a simple course

创建者 Chris M

Apr 09, 2016

I've already written a review but it seems to have been removed...

This is an awful course, there is very little purpose to it whatsoever, it is basically a module in markdown which will in all honesty not have much application for most learners.

In addition, the course is not at all balanced / laid out well, there is a peer assignment in week 1, which you need to have covered week 2's content for.

Lastly, the recording quality of some of the lectures is awful, it is clear that they have simply used some recordings of an actual classroom session of a related course instead of recording for Coursera.

In all honesty, this entire specialisation is of awful quality, it is not a data science course, it is a "here's a few useful things in R" course, and the instructors should be ashamed that their institution makes money from it.

创建者 gerson d o

Jun 21, 2019

GREAT course!!!!!!!!!!!!!!!!!!

创建者 Avolyn F

Jun 19, 2019

I was really passionate about the subject matter, but, although I have experience in R, apparently not enough to complete the assignment. Would have liked a little more warning that this would be needed, I was more interested in the topic of Reproducible Research, which while I agree is easier done via code of some kind, shouldn't be a topic specific to R, should be applicable to Python, SQL, whatever.

Might have time to revisit this, but will probably need to take a few more R classes to even be able to complete, likely won't get around to it, but the first 2 weeks were worth the cost of paying for a certificate, I guess.

创建者 Edouard A

Jun 18, 2019

Interesting projects

创建者 Rok B

Jun 17, 2019

Not the most important course in the series, but I give it 3 stars.

Positives, I'm impressed with RMarkdown. It is a handy tool to make reproducible research. I also think the final assignment was very interesting. You can train cleaning data.

Negatives, lectures from weeks 3 and 4. They are poorly recorded and have little to no value for the course

创建者 Nino P

May 24, 2019

To be a data scientist you must use RMarkDown. Here you learn how to use it. A must do course for data scientists and highly valuable.

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 Israel D D G

May 16, 2019

Good material

创建者 Rooholamin R

May 13, 2019

lectures are a little bit theoretical and at some point maybe boring but projects will give you a real experience with data and research reproducibility.

创建者 Alán G B

May 02, 2019

A very useful course. It helped me to improve the way I structure the analysis at my current job, especially by keeping track of every transformation I apply to the data I’m working with.

创建者 Akram N

May 02, 2019

Very fruitful. I enjoyed this lesson very much.

创建者 Kehinde U

Apr 30, 2019

Nice course

创建者 Manuel E

Apr 29, 2019

Good - Makes you assimilate the concept and work on it

创建者 Charles M

Apr 25, 2019

Great course. This and the previous course in the data scientist specialization are extremely practical and I've found immediate utility in my career.

创建者 Chetan T

Apr 22, 2019

This course is very helpful in terms of not only doing the analysis but also getting to know the finer nuances of making a structured markdown document for future reproducible.

创建者 Sri H

Apr 21, 2019

Good

创建者 carlos j m

Apr 12, 2019

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

创建者 Naren R B

Apr 08, 2019

Would definitey recommend this, it covers an important aspect of research for Data Scientists.

创建者 Andrew

Apr 07, 2019

One of my favorite courses in the specialization so far.

创建者 Fidel S C

Mar 20, 2019

Very good course

创建者 Paul R

Mar 13, 2019

Along with the principles of "reproducible research", the primary tool introduced in this course is knitr to produce reproducible research papers and Rpubs for publishing papers. I think this specialization covers RMarkdown 3 different times. Assignments were good, at this stage you start to produce proper papers on an analysis topic which is very much needed before hitting the statistics/regression lectures; however this material can be compressed and needs to be combined with the 9th course which covers Rmarkdown/RStudio again.

创建者 Thej K R

Mar 12, 2019

Nothing serious in this course! Rmd is a good tool to work with! and get familiar with!

创建者 Francisco M R O

Mar 09, 2019

It was very useful for me, now I know the importance of making data analysis reproducible.

创建者 Matthew S

Mar 05, 2019

I often feel like people completely ignore the "science" aspect of data science (read any data science career question on quora for example). This course does an excellent job of introducing key aspects of the scientific method that you might not have encountered if you've never done an experiment before. The final project is a lot of work (mostly data cleaning) but very fun and informative.