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Opiniones y comentarios de aprendices correspondientes a Investigación reproducible por parte de Universidad Johns Hopkins

4.6
estrellas
4,070 calificaciones
586 reseña

Acerca del Curso

Este curso se centra en los conceptos y las herramientas que permiten realizar análisis de datos modernos de forma reproducible. La investigación reproducible se basa en la idea de que los análisis de datos y, en general, las afirmaciones científicas, se publican con sus datos y el código del software para que otros puedan verificar los hallazgos y basarse en ellos. La necesidad de reproducibilidad aumenta drásticamente a medida que los análisis de datos se vuelven más complejos, con conjuntos de datos más grandes y cálculos más sofisticados. La reproducibilidad permite que las personas se centren en el contenido real de un análisis de datos, en lugar de en los detalles superficiales que aparecen en un resumen escrito. Además, la reproducibilidad hace que un análisis sea más útil para otros, ya que los datos y el código que en realidad permitieron llevar a cabo el análisis están disponibles. Este curso se centrará en las herramientas de análisis estadístico alfabetizadas que permiten publicar los análisis de datos en un único documento que permite a otros ejecutar fácilmente el mismo análisis para obtener los mismos resultados....

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."

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51 - 75 de 568 revisiones para Investigación reproducible

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.

por Deleted A

21 de sep. de 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.

por Arindam M

20 de mar. de 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.

por Scipione S

7 de jun. de 2020

Wonderful course. Maybe you could enlarge the part related on litarate programming, and you could change the position in Data Science Specialization. I also think, it would be better to arrange it after Statistical Inference and Regression Models courses.

por Eduardo A

10 de feb. de 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.

por Arunkumar M R

11 de sep. de 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.

por Dan K H

11 de abr. de 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.

por Lowell R

7 de oct. de 2016

This is an excellent course which teaches you fundamental best practices in research. After completion you will look back at early scripts in horror! It's unfortunate that the practices you learn aren't more widely practiced.

por Ray L J

2 de may. de 2017

My favorite course in the degree, so far! I started applying what I learned immediately. This course should be mandatory for any data analyst, as the concepts are applicable no matter which language or tools are used.

por 坂本幹次

29 de sep. de 2020

There are important things you usually dismiss.

The lecture is great and it really deserve professional course.

This lecture is potentially hard, so you should spend a lot of time for the lessons and course projects.

por Jordan I

8 de ene. de 2020

Great course that provides a structure for analysis and how to challenge the analysis. I found the assignments hard. The lack of information about the data for the assignment represented a real-life situation.

por Jorge B S

20 de ago. de 2019

I have found this course very useful in order to learn the keys of reproducible research. Moreover, both course projects are useful for putting other skills of this specialisation into practice. I recommend it!

por Regis O

29 de ago. de 2016

This course had a profound impact on the procedures I use for data analysis. It provides best practices for documenting the steps of the analysis to ensure accuracy and quality. I highly recommend taking this.

por James S

21 de may. de 2016

This has been a great introductory course into reproducible research. The topics were clearly carefully chosen and wisely integrated to create a smooth flow. Money well spent and invaluable knowledge gained!

por Marco A M A

8 de mar. de 2016

It is a nice introduction to some important issues in scientific research, may not be so intresting to non academic data scientist hopefuls, but it is very important, I think to those that are within academia.

por Rumian R

20 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."

por Alán G

2 de may. de 2019

A very useful course. It helped me to improve the way I structure the analysis at my current job, especially by keeping track of every transformation I apply to the data I’m working with.

por Araks S

23 de jun. de 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.

por Chetan T

22 de abr. de 2019

This course is very helpful in terms of not only doing the analysis but also getting to know the finer nuances of making a structured markdown document for future reproducible.

por Ramesh G

30 de abr. de 2020

Great topic which is discussed well with a good case study. I'd like to see more up-to-date content and more detailed analytical techniques. However, it's a nice introduction!

por Ryan H

21 de ago. de 2017

I personally got a lot out of this, both from a philosophical perspective and a nuts-and-bolts perspective. And I got to practice a lot of stuff learned on earlier courses.

por Dylan E

5 de ago. de 2017

Very informative and enjoyable class. The importance of reproducible research is stressed clear and concisely, Roger D. Peng does a great job of explaining the material.

por Vishwamitra M

14 de feb. de 2020

Highly recommended for beginners to learn the basics of Data Science, Re-producibility and how to write a good report around the analysis done by you as a data analyst.

por Pierre S

11 de abr. de 2017

I think this not a complicated course but is absolutely fundamentals of proper scientific principles which are so often lacking in many data science/analytics projects.

por Juan P L R

25 de sep. de 2020

Great course to learn about reproducible research in R, using knirt and RPub. Excellent course and carefully designed to complement the Specialization of Data Science.