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Отзывы учащихся о курсе Воспроизводимое исследование от партнера Университет Джонса Хопкинса

4.5
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Оценки: 3,575
Рецензии: 510

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.

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1–25 из 493 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 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: 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: 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: Dzmitry S

May 10, 2016

Too expensive for such a simple course

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: Ishwarya M

Aug 10, 2019

Without taking this course wouldn't have fully understood the importance of reproducible research in data science. Thank you so much. I recommend this course for all data scientists.

автор: Joe D

Aug 01, 2019

The two projects were interesting and built on skills learned in the previous four courses in this specialization that focused on using the R language. The video lectures were largely repetitions of the course text, which is fine, some people prefer videos, others prefer texts. (I read the text and was fine with skipping around the videos and/or playing them at 2x speed.) Perhaps the most useful skills learned in this course were during the projects where we did some data cleaning and analysis, then wrote up our results in an R markdown file (.Rmd) and published to Rpubs. Overall enjoyable experience. The most useful "hack" was learning how to preserve markdown files in the Rstudio settings so that when you push your .Rmd s to github, you get a nice readable markdown file.

автор: Do H L

Jun 17, 2016

An informative course that will teach you the paradigm of reproducible research, this is very important in Data Science.

You'll learn how to write a data science report using R markdown. That's not the most important thing though. The most important thing is knowing how to start your research and what to do with the data to come up with valuable insights. That process includes getting and cleaning data, manipulation, processing of data, analyzing the data and drawing inference from results.

Great course for people who are looking to be serious data scientist! Rigor and thorough, this is a very good introduction to report writing in R.

автор: Nataliia M

Jan 24, 2017

This course was not so convient for me as other Data Science Courses. Therefore it even seems easy than the rest of speciality I was not able to get to the end until first assignment has been moved to the 2nd week. But starting and leaving it for few times I got so needed experience with report preparation, reseach process description and converting all the steps of my study into a story, which could be understandable for someone else. Thanks everyone! I enjoy my new skills and sell them for good price!

автор: Diego-MX

May 23, 2016

This course presented a very useful challenge in terms of data analysis. In one of the projects we're given a data-set that doesn't show straight up what to do with it, even with a clear set of instructions. And this is something common in terms of data analysis that everyone eventually faces with. This isn't the kind of difficult in terms of technicality, but still took me a couple of times to complete because of the maturity and independence needed to just "tackle" the data. I highly recommend it.

автор: Alejandro C T

Mar 18, 2016

This is a great course that teaches students about the importance of reproducibility for research and data analyses. It provides tools that help a scientist to thoroughly document and publish their research in a fully reproducible way and it shows the current best practices for reproducibility.

All the course content is very clearly explained and the assignments help reinforce the students' learning process.

автор: Jose A R N

Oct 20, 2016

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career.

Please, take a look at my LinkedIn profile: https://www.linkedin.com/in/joseantonio11

I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors.

автор: Antonio F

Jan 03, 2017

This course has changed my way to look at everyday's work. Not only with respect to the explaination to others of your work, why you take an approach compared to another, what is the sequence of your data cleaning and processing etc. It is especially useful to avoid the pain of re-learning by ourselves what we learned 6 months, one year, two years ago. Great course, one of the best of the specialization.

автор: Kalle H

Dec 07, 2017

Very good. Could go deeper in some areas but gives a good introduction to Rmarkdown, knitr, and general information about how to make research reproducible and why this is important. The course is of not only of great value for academic students but also of value to people working in a commercial industry. The coursework was good and I learned a lot from looking at how others tackled the same problem.

автор: João F

Dec 25, 2017

Great course on how to document and report an analysis, from getting access to raw data up to the presentation of the findings. knitr and R Markdown are very good tools for reporting. As working with data becomes more complex it's of the utmost importance that Data Analytics Professionals present their work in a reproducible manner. Dr. Peng is an excellent professor and online instructor. Thanks!

автор: David N

Nov 22, 2016

Reproducibility of results is a cornerstone of the scientific method. Full reproduction is not always practical, but in 2016 it should almost always be possible to present the computations behind research so that others may retrace and validate the steps taken. Professor Peng is a thought leader in this idea and provides a well-designed introductory course in "Reproducible Research".

автор: Shashikesh M

Jun 30, 2017

My experience of taking this course was really challenging and great, today I got to know how it is critical and crucial to Reproduce exact result as per author original research. I also got to understand that after having all analysis, but if your codes are not reproducible then your work has no good value. Reproducible Research is one of the most important part of data science.

автор: Sujata E

Dec 11, 2017

I found this class very useful. While it may be easy for some to pick this up on their own, I thought Roger's take on it, as well as the other instructors, impressed on the critical nature of Reproducible research. I found the lessons and the final project valuable in breaking down my own weaknesses in documenting and discovering new aspects of R. I highly recommend this class.

автор: George G A

Aug 20, 2017

Loved it! I am not as technical as others in my class, so I struggle a bit with the programming part. However, I understand the importance of and now how to perform Reproducible Research in an industry-wide format. The examples given in the videos, especially regarding medical studies gone awry, stress the importance of attention to detail and reproducibility.

автор: Juan C L T

Nov 09, 2017

Great course. It provides learner with the knowledge and skills needed to be a modern scientist/analyst, focusing on making analyses reproducible from the beginning to the end. The final project is challenging in terms of proper data cleaning, and it may take much more than 2 hours to complete it adequately. It is of great value to take this course seriously.

автор: Kristin A

Oct 31, 2017

Great focus on learning how to publish and communicate our results! It was a bit of a review for me because I have published in the scientific literature before, but it's a great intro for people who are new to this. I am very happy that this focus on reproducible research and communicating results is part of the curriculum here.

автор: Ailsa D

Apr 06, 2018

I think this course is very useful and relevant for data scientists and analysts. In order to verify that valid conclusions have been reached, it is vital that analysis can be reproduced. The final project was very interesting and taught me a lot about how I approach analysis projects, and how to improve this going forward.

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.