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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

3,654 个评分
521 条评论


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....



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.


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.


26 - 可重复性研究 的 50 个评论(共 504 个)

创建者 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".

创建者 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.

创建者 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.

创建者 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.

创建者 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.

创建者 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.

创建者 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.

创建者 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.

创建者 Carlos M

May 01, 2017

Great course with good case studies and clearcut goals - learn knitr, markdown, and a little history of literate programming languages. The importance of making Reproducible Research is ineffable. But, reproducible research won't do anything if the analysis is wrong- this is covered in data pipeline lectures in week 3.

创建者 Pavel P

Feb 01, 2016

First I've been thinking that the topic is not so important to create a whole course for it, but after watching it and trying some of the technics mentioned here I found that the quality of my analysis increased , also now I spend much less time trying to organise my scripts, data and findings. Thanks!

创建者 Roberto D

Nov 18, 2016

This was a great course, it proves the value of reproducible research. Case in point, the lecture on where cancer trials were cancelled due to analysis results. This trial withdrawal undoubtedly helped people, either by saving lives or at the vary least not aggravating their condition.

创建者 Emmet C

Sep 06, 2017

Not much fun but very important. This class forced me to learn Git (finally) and to really think about why and how I should make all of my projects reproducible. As usual with this specialization there are a couple of analysis projects thrown in to make sure you're not getting rusty.

创建者 Tai C M

Sep 25, 2017

I really like this course despite a minor misunderstanding. I did a lot of researches in technical analysis of the financial market and I have not found a solution to document all my researches and findings. Looks like R is the way to go and I will be using the Rmd very frequently.

创建者 Evgeny P

Feb 23, 2017

I found this course is really important and good. Most valuable takeaway is making you think about other people who will look / challenge or derive your analysis. Looking at work you are doing from this perspective lets you keep things in order and stay as transparent as possible.

创建者 Rishabh J

Aug 28, 2017

The final course project itself was worth taking the entire course. It exposed me to an extremely messy real world data set and what kind of impurities there can be in a data set. Apart from that , the course content was not much and could be completed only in a couple of days.

创建者 Chiradip

Jul 09, 2017

This was such an easy but important course. I think most people would just want to get over with it but it is very important to learn how to reproduce the results. I could totally see why the professors thought to include this in the course although it is not specific to R

创建者 Yusuf E

Feb 02, 2018

This course spanned a single but important topic. The assignments were really important and challenging ( I spent several days on the second one). Overall, a fun course but don't expect anything like R Programming or Getting and Cleaning Data in terms of usefulness.

创建者 Молоков М В

Mar 28, 2017

Данный курс помог мне узнать о новой полезной функции языка R и R studio - создании отчетов с одновременной возможностью анализа данных. Использование данной функции - генерации результатов обработки данных в pdf, word, html файл облегчает работу и анализ данных.

创建者 Esteban R F

Oct 24, 2019

The projects in this course were a real challenge, which demanded to tackle those problems with a mind willing to go to the hedge and discover new horizons. The result was that I ended up the course with real skills for processing data in a reproducible process.

创建者 Angela W

Sep 15, 2017

Despite this being the course with the lamest name (sorry), I really enjoyed it! I learned a lot of new stuff and also got to apply the things that I learned in the previous courses (especially Exploratory Data Analysis), so I feel that this was time well spent.

创建者 Deleted A

Sep 21, 2018

The course was fantastic. I realized the power that a Data Science Analyze can create. In this module in particular, I was even more interested in completing the specialization. Thank you Professor Roger Penn and the entire team of teachers for their teachings.

创建者 Arindam M

Mar 20, 2017

A great course which might not draw the right attention while moving towards a data scientist role. But without a great deal of focus on the communication of analysis is even more important to gain buy-ins within or outside the org. Will keep them in mind.

创建者 Eduardo d S A

Feb 10, 2017

This course makes us re-think things that we take for granted. I was shocked in the beginnig on how we ignore practices that should be the basics of any research. As the course progress, I learned new concepts what is essential to our self development.

创建者 Arunkumar M R

Sep 11, 2017

I guess from the case studies and research on the web whats I learned from this course is the importance of reproducible research is. This course explains the importance of it and the ways to achieve it easily and concisely. Thanks for the authors.

创建者 Dan K H

Apr 12, 2016

This turned out to be one of the more fun courses, especially listening to Rogers lecture "live in class room" and also the case presented by M.D. Anderson was great. I really enjoyed this course, even more than I initially expected to.