Chevron Left
Volver a Reproducible Research

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

4.6
estrellas
3,840 calificaciones
547 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

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.

RR

Aug 20, 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:

76 - 100 de 532 revisiones para Reproducible Research

por Subramanya N

Dec 12, 2017

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!

por Johann R

Jun 07, 2017

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.

por Md A I

Sep 22, 2020

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

por Camilo Y

Jan 10, 2017

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.

por Andrea G

May 11, 2020

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.

por Devanathan R

Feb 07, 2016

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.

por Charles M

Apr 25, 2019

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

por Marco A I E

Sep 20, 2018

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.

por Jessica R

Aug 12, 2019

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

por Connor G

Aug 30, 2017

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.

por Praveen k

Oct 19, 2018

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

por Marco B

Dec 05, 2017

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!

por Charly A

Nov 26, 2016

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

por Warren F

Aug 16, 2016

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

por Prairy

Mar 17, 2016

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

por Tine M

Jan 23, 2018

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

por Anirban C

Aug 15, 2017

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

por Nino P

May 24, 2019

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.

por Keidzh S

Apr 24, 2018

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

por Leandro F

Feb 28, 2017

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

por Arjun S

Aug 27, 2017

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

por Daniel C J

Nov 14, 2016

Great course. A must for every analyst for its simple tips on reproducibility, which can go a very very long way at work or school

por Omar N

Nov 08, 2018

Really good module/course, gives you a glimpse into real world implementation of data science and the challenges involved with it.

por ONG P S

Jan 19, 2020

Very practical and knowledge learned can be applied into my works as auditors. This can benefit any fields involving using data.

por Donald J

Jan 22, 2018

These are important skills for a data scientist and I'm glad there is a full 4-week course dedicated to reproducible research.