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Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

4.6
звезд
Оценки: 3,951
Рецензии: 564

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

AA
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.

RR
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:

76–100 из 547 отзывов о курсе Воспроизводимое исследование

автор: Christian H

10 нояб. 2016 г.

This course helped me realize why reproducible research is absolutely necessary, and gave me the tools to implement reproducibility in my work. Project was great.

автор: Himanshu R

25 янв. 2018 г.

A good informative course to inform about importance of "Reproducible Research", also a good one for practicing code writing and publishing in RPubs and Github.

автор: Joshua M

4 мар. 2016 г.

This class's R markdown material taught me to efficiently convey and market data analysis to non-specialists of data. It was immediately valuable to my career.

автор: Subramanya N

12 дек. 2017 г.

Good info on RStudio & RR.

I can easily figure out who has attended this course by their methodical nature and work when I see Kaggle competitions. Great job!

автор: Johann R

7 июня 2017 г.

A handy course to do when you have to create and submit reports with calculations and code. Learn the basic principles of report writing and report structure.

автор: Md A I

22 сент. 2020 г.

Though I could not solve all course projects on my own, I at least understood the techniques and enjoyed doing the course greatly. Thanks to the instructors

автор: Camilo Y

10 янв. 2017 г.

I found all the topics of this course important. Not only for my professional career but also for everyone who is involved with data and science in general.

автор: Andrea G

11 мая 2020 г.

Very important course. Not so many fancy analysis but it introduces to Markdown and explains well what does it mean to do data science within a community.

автор: Devanathan R

7 февр. 2016 г.

a very important part of data analysis. I especially found the case study in week 4 to be of tremendous interest highlighting the real world applications.

автор: Charles M

25 апр. 2019 г.

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

автор: Marco A I E

20 сент. 2018 г.

Very interesting, the fact that our research procedure can be explained and showed to other to reproduce, validate and work on top of it is fantastic.

автор: Jessica R

11 авг. 2019 г.

Very useful in bringing together skills learned in the earlier courses of the Data Science specialization: R programming, R Markdown, knit, RPubs.

автор: Connor G

30 авг. 2017 г.

Very important subject matter taught well. My only qualm is that the final project was more difficult than I expected it to be given the content.

автор: Praveen k

18 окт. 2018 г.

Good course. Examples given throughout the course are biological based so it is little hard to understand completely because they are technical

автор: Marco B

5 дек. 2017 г.

this course is incredibly useful!

in my job i practice data analysis everyday and this course helped me to do everything in a more efficent way!

автор: Charly A

26 нояб. 2016 г.

Excellent content and plan. The delivery is fantastic and the professor's explanatory clarity is top notch. I highly recommend this course.

автор: Warren F

16 авг. 2016 г.

Slightly less information than the previous courses in DS spec but important for someone who has not done scientific research in the past.

автор: Prairy

17 мар. 2016 г.

Excellent course that is both well presented and very clear, providing many examples and opportunities to practice throughout the course.

автор: Tine M

22 янв. 2018 г.

Very interesting course, I was able to apply what I learned in the previous courses of the specialization, and that was a good exercise.

автор: Anirban C

15 авг. 2017 г.

Nice course! It helped me to understand the concepts of markdown and related R modules. The assignments were challenging and fun to do.

автор: Nino P

24 мая 2019 г.

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.

автор: Keidzh S

24 апр. 2018 г.

Thank you so much. Representatives lessons in my opinion very effective. I learn so much about html and markdown files in this course.

автор: Leandro F

28 февр. 2017 г.

One of my favourites. The course is easy to follow and the idea of having a self-contained and reproducible document is very powerful.

автор: Luz M S G

6 окт. 2020 г.

It was a good experience. The final project has been the most challenging that I have had in the specialization, but I learned a lot.

автор: Arjun S

27 авг. 2017 г.

Great stuff. Glad to have the course make us create an Rpubs profile and publish research. Recommended strongly for data scientists