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Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

Оценки: 4,045
Рецензии: 577

О курсе

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....

Лучшие рецензии

12 февр. 2016 г.

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.

19 авг. 2020 г.

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."

Фильтр по:

501–525 из 559 отзывов о курсе Воспроизводимое исследование

автор: Ekta A

23 февр. 2018 г.

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.

автор: Rashaad J

3 окт. 2017 г.

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.

автор: Hua-Poo S

19 февр. 2017 г.

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.

автор: Tony W

16 июля 2016 г.

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.

автор: STEVEN V D

12 дек. 2017 г.

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.

автор: Brittany S

1 нояб. 2018 г.

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.

автор: Rose G

31 мар. 2020 г.

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.

автор: Michał M

28 янв. 2016 г.

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.

автор: Andrea E R V

23 авг. 2018 г.

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.


1 сент. 2017 г.

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.

автор: Joseph C

8 февр. 2016 г.

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

автор: Andreas S J

4 окт. 2017 г.

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.

автор: Fabiano G d S

7 мар. 2016 г.

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

автор: James O

31 окт. 2016 г.

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

автор: Fabiola J C

9 янв. 2021 г.

I experience that the course does not cover all the necessary tools to tackle the final assignment with ease.

автор: Diego T B

17 нояб. 2017 г.

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

автор: Robert K

12 июня 2017 г.

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

автор: Raushon K

17 февр. 2016 г.

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

автор: Nathan M

11 июня 2016 г.

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

автор: Jingqin L

28 апр. 2021 г.

Cover some essential issue in reproducible research but don't touch much on some details.

автор: Rohit S A

20 окт. 2016 г.

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

автор: Fernando M

4 сент. 2017 г.

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

автор: Corbin C

23 апр. 2018 г.

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

автор: Shuwen Y

9 июля 2016 г.

content is not enough for one class. should be only one to two videos.

автор: Martin G

3 авг. 2019 г.

Interesting content. However, it can get somewhat repetitive.