Chevron Left
Вернуться к Воспроизводимое исследование

Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

4.5
Оценки: 3,464
Рецензии: 496

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

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.

Фильтр по:

1–25 из 479 отзывов о курсе Воспроизводимое исследование

автор: 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.

автор: 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.

автор: Dzmitry S

May 10, 2016

Too expensive for such a simple course

автор: Ashwath M

Mar 20, 2016

I felt this course could have been added as sections to other courses. One separate course for this topic is a waste of money.

автор: Michal K

May 12, 2016

1 for Knitr, otherwise it's waste of time.

автор: Chandrakanth K

Oct 07, 2017

I don't think it requires a separate course for this topic. possibly combine it with other courses and introduce neural networks.

автор: Matthew P

Dec 18, 2016

Whilst I can see why the idea of reproducible research is important there was't really enough material in this course for the full four weeks - and in fact a lot of the videos repeated the same information.

автор: Bruno R d C S

Jan 22, 2019

A great introduction to basics of scientific method concerning statistics and result reporting.

автор: Chanpreet K

Dec 30, 2018

Good course content. All things explained quite well.

автор: Anusha V

Jan 03, 2019

Excellent Course - particular useful for anyone doing research and performing any kind of analysis on the observed data.

автор: Alzum S M

Jan 08, 2019

A great course that will take you ahead to be a Data Scientist

автор: Glenn W

Mar 04, 2019

Favorite course so far. Really enjoyed working on the projects. They were very helpful in helping to reinforce the material.

автор: Fidel S C

Mar 20, 2019

Very good course

автор: Andrew

Apr 07, 2019

One of my favorite courses in the specialization so far.

автор: Naren R B

Apr 08, 2019

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

автор: Azat G

Jan 24, 2019

Amazing course, it introduced the concepts of reproducibility which is used to provide scientific fairness, transparency.

автор: carlos j m

Apr 12, 2019

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

автор: Sebastian R

Apr 11, 2017

Great perspective of how to do science!

автор: Edith

Jun 14, 2016

Great course! I learned a lot. Thx.

автор: Christian H

Nov 11, 2016

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: Lindy W

Dec 19, 2016

Learnt some really neat new tools. Favourite course from this specialisation so far.

автор: Md. I H

Jul 04, 2017

This course provides insights about how to reproduce the research findings in efficient manner.

автор: Prairy

Mar 17, 2016

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

автор: Robert D

Nov 14, 2016

In my opinion, this is one of the most valuable courses in the Data Science Specialization. The principles of tidy data and reproducible research are critical and this course makes an excellent presentation of both. I have only just completed the course and have already begun using what I learned in my professional life.

автор: Jennyfer C

Dec 14, 2016

Excellent course! I highly recommend it.