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

4.6
estrellas
4,105 calificaciones
597 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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501 - 525 de 579 revisiones para Investigación reproducible

por Khobindra N C

18 de may. de 2016

Excellent

por Anup K M

2 de oct. de 2018

good

por Greg B G

21 de sep. de 2017

nice

por Rajib K

28 de mar. de 2017

u

por Miguel C

6 de abr. de 2020

I enjoyed this course, especially the tips we got on how to make our analysis more reproducible and the practice with RMarkdown and RPubs. The lecturer was really knowledgeable and engaging, making it easier to follow the course. The assignments were challenging and allowed me to build on things I had learned in previous courses, especially R skills.

My biggest problem with this course was its repetitiveness. Its content repeats some of what was explained in Data Scientist's Toolbox course, and sometimes it repeated things from previous week of this same course. I think the material can be summarized into just 2 or 3 weeks. I also found the second course project quite exhaustive; it wasn't particularly hard but the data was quite messy so it took a long time to clean, which was boring and tiring but I guess that's part of the data scientist's job.

Overall, I still enjoyed the course and I would recommend it to other people interested in becoming data scientists.

por Christiane H

8 de dic. de 2015

Overall a good course for self-study. The assignments in particular are excellent for data cleaning, analysis and interpretation. The quizzes are very basic though and appear to be there only to check if the student has gone through the lectures. The knowledge needed to answer the quizzes and achieve the desired results in the assignments are vastly different and should be addressed.

The case studies at the end are insightful and more use could be made of them in a more advanced course. There is a lot of repetition of concepts throughout the course and this can become distracting. THe format for the lecture videos varies throughout and this inconsistency (along with extreme audio volume changes) also becomes distracting.

Other than that, excellent for driving the need for reproducible research (RR) home, presenting and explaining some tools available to achieve RR and ways of publishing results/reports from these studies.

por James T

26 de abr. de 2016

The course was good. I enjoyed it. The biggest problem was the un-moderated participation of at least one other student. This particular student drove the discussion of assignments, leaving little room for others to explore, ask, and answer questions. As far as I know the student was not a mentor/TA, but It would have been most helpful for staff to weigh in on some of the student's post. I really believe the student was feeding his/her ego.

por Olivia U

21 de may. de 2020

This course comes as the fifth one in the series, but the stuff in there is pretty basic, and we've worked before with R markdown in the series. It's important stuff, but I would put it earlier. The content focuses a bit too much on publishing research, which is surely important in academics, but not so much for people like me who work in regular companies. That's not my favorite course so far.

por Blazej M

3 de dic. de 2017

Course assignments are great and one can learn a lot by doing them. One warning: there is no way to finish second assignment in 2 hours as it is specified in the course. It took me almost a week of digging and clearing data! But I enjoyed it.

Video materials are mediocre at best. With the exception of last video on genes sequencing . That one was entertaining.

por Rok B

17 de jun. de 2019

Not the most important course in the series, but I give it 3 stars.

Positives, I'm impressed with RMarkdown. It is a handy tool to make reproducible research. I also think the final assignment was very interesting. You can train cleaning data.

Negatives, lectures from weeks 3 and 4. They are poorly recorded and have little to no value for the course

por Shengyu C

21 de mar. de 2016

The content is good but the contents have a lot of overlap. The instructors are definitely knowledgeble about the material and clear about the presentation but a lot of the same matierial are repeated throughout the course unintentionally. Think the insturctors just thought it was enough to throw a bunch of things together and called it the day.

por George A

6 de feb. de 2016

To be honest, I couldn't realize why this had to be an entire seminar on its own. Apart from that, in some videos the audio quality was rather poor and the instructor seemed to have caught some cold or something. Although the topic of the course is interesting and significant, I think that do far it is the least engaging of the specialization...

por Robert R

25 de abr. de 2016

Course is generall very good and lots of fun!

2 things i would change:

... the Assignment 1 is too early in the course or the Lectures are disordered, but I needed the second and third week material to do the first weeks assignment.

... Add Jupyter Notebooks in the Specialization in addition to Knit-R

por Jaromír S

6 de jul. de 2017

It helped me to organize my research stuff better. But it's too long course for subject which is not enough for 4 weeks. So, it's relaxing topic. It would be enough to do just 1 peer assignment to learn everything. Most of it I knew, but there is always something new which I have learned.

por Michalis F

21 de may. de 2016

Too expensive for the material it provides; it is helpful and necessary but this course can be summarised in 1-2 lectures. There is a very good lecture from an external speaker,which was very good and funny (at least i found it funny) and i didn't realise that it was 30 mins long.

por Fabiana G

23 de jun. de 2016

Course content is okay - there was some repetition of topics throughout the weeks. As the other first courses in the specialization, students would benefit tremendously if the instructors were a bit more active - the course feels out of date and abandoned.

por Rafael S

27 de oct. de 2017

This was, by far, the hardest course in the specialization until now. Not because of its dificulty per se, but because it was too boring, there where very little practical exercises, and I just had to gather all my willpower to get to the end of this one.

por Moshe P

21 de may. de 2019

The course seems to be based on lectures recorded at different times. Some points discussed are repetitive. the quality of content is good though. I believe the whole material may have to be updated and, potentially, re-recorded.

por Ekta A

23 de feb. de 2018

Most of the knowledge one needs can be perceived till week 2 only. Week 3 is a complete repetition of previous 2 weeks. While week 4 offers case studies which I feel are not much important. But overall the experience was good.

por Rashaad J

3 de oct. de 2017

This is a good course for people who don't have experience with conducting research. For experienced researchers, the content provided is not too informative. More discussions on R Markdown should have been provided.

por Hua-Poo S

19 de feb. de 2017

I had difficultly with the two assignments, not because they were difficult but because the instructions were not clear. From reviewing other's assignments, it did not appear to be just me.

por Tony W

16 de jul. de 2016

Has interesting ideas and approach to forming a structure way of analysing a problem. The module does feel a little thin in content, and perhaps should be combined with Exploratory Analysis.

por STEVEN V D

12 de dic. de 2017

A bit too much focused on academic research, I find. Quality of the video's isn't always top-notch either.

Good exercises to practice plotting skills with interesting, real-life data sets.

por Brittany S

1 de nov. de 2018

I wish they'd stop labeling the course projects as two hours. The week 4 project took a lot longer than that (closer to a week). Also, a lot of the information presented was repetitive.

por Chris N

10 de ene. de 2022

C​ourse content was relevant. Quality of interactions / other students work was very poor. I had to report twice as many students for plagerism than had valid scripts to assess???