Chevron Left
Volver a Reproducible Research

Opiniones y comentarios de aprendices correspondientes a Reproducible Research por parte de Universidad Johns Hopkins

4.6
estrellas
3,956 calificaciones
564 reseña

Acerca del Curso

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....

Principales reseñas

AA
12 de feb. de 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.

RR
19 de ago. de 2020

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."

Filtrar por:

26 - 50 de 547 revisiones para Reproducible Research

por João F

25 de dic. de 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!

por David N

22 de nov. de 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".

por Shashikesh M

30 de jun. de 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.

por Sujata E

11 de dic. de 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.

por George G A

20 de ago. de 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.

por Juan C L T

9 de nov. de 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.

por Marco M

26 de sep. de 2020

This is a very interesting course, and I learned much. My skills in markdown improved considerably and I also learned more about RPubs and other platforms. The only thing that could be better are some video lectures, which are too long for a MOOC. Some of them were also recycled from in-person lectures captured on video, which is not ideal.

por Keith H

18 de jul. de 2020

I thought this course was great. I think most people do not think about reproducibility of research, how one might achieve it, and why it is so very important. I thought the video on the cancer research was particularly illuminating and showed someone trying to get away with something by hiding as much of their analysis as they could.

por Tomer E

1 de jul. de 2020

I think some of the lectures kind of repeat themselves and I got the point quite from the beginning. On the other hand, the case study about Duke really made an impact on me and really made this course worth while. I also really liked the second course project, which really got me understating how to use knitr, rmarkdown and rpubs.

por Kristin A

31 de oct. de 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.

por Ailsa D

6 de abr. de 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.

por Robert D

14 de nov. de 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.

por Carlos M

1 de may. de 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.

por Sidclay J d S

14 de jun. de 2020

I have learned a lot in this course, for me more important than any tool or technical skill it was the idea of reproducible research, which can be used in many other process. Anyone who has been in a deputy position or replacing someone else at work can easily understand the importance of making things reproducible.

por Pavel P

1 de feb. de 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!

por Roberto D

18 de nov. de 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.

por Emmet C

6 de sep. de 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.

por Tai C M

25 de sep. de 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.

por Evgeny P

23 de feb. de 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.

por Rishabh J

28 de ago. de 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.

por Chiradip

9 de jul. de 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

por Yusuf E

1 de feb. de 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.

por Молоков М В

28 de mar. de 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.

por Esteban R F

23 de oct. de 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.

por Angela W

15 de sep. de 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.