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Avis et commentaires pour d'étudiants pour Statistics for Genomic Data Science par Université Johns-Hopkins

4.2
étoiles
248 évaluations
45 avis

À propos du cours

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University....

Meilleurs avis

ZM

Jun 28, 2018

The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!

CJ

Jul 16, 2019

It is really great that told me lots of basic statistical information that I didn't know.

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26 - 42 sur 42 Avis pour Statistics for Genomic Data Science

par YuanL

Sep 08, 2019

Thanks!

par Ryan A H

Feb 12, 2017

Overall, a very good course. Not without its flaws (inconsistent video audio levels), but I have walked away knowing far more about Genomic Data Science than I expected to.

par Nitin S

Feb 19, 2019

sometimes termininology was used interchangeably, which can be confusing for a beginner but overall a good introduction to statistcs for genomic data analysis

par Saaket V

Feb 19, 2018

Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.

par eman m a

Jul 01, 2020

theoretical parts need more explanation. But in general, It is a well-structured course. thanks for your efforts

par Maria J

Aug 08, 2020

Very helpful and i understood i should master statistics and do more research

par tawanda n

Jan 23, 2020

new material would be great as well as new datasets

par Pedro M

Mar 26, 2020

Pretty good but a little superficial and outdated.

par Michael R D

Feb 12, 2017

Nice course. Ready to apply data.

par Ariful l

Aug 12, 2019

good for learning

par NAMRATA P

Apr 10, 2019

good

par Dr. P R I

Mar 01, 2019

good

par Mihaela M

Jul 16, 2020

I liked how energetic the lecturer was. He clearly has a reasonable amount of experience and some of the tips he gave about doing statistics in the context of genomic studies were useful! I liked how the professor recommended some extra reading at the end of every topic. I also really liked the fact that he recommended some extra courses to be taken.

But despite that, the course itself was a bit too short - the topics introduced just scratched the surface. This made sitting through the R tutorials particularly tedious - how would one get the use of R tools to do the tasks, if they haven't understood the theory properly? I know it's supposed to be an intro course, but still in its current state it can be a bit confusing. I would suggest making it somewhat longer, so that the intro to each topic could be done a bit more in depth - maybe focusing a bit more on the theory, so that the students could get an intuition for the methods, rather than just doing R commands which for them mean nothing if the theory is still very blurry.

par Catherine J

May 19, 2020

Because of short time frame for course, it couldn't present topics in sufficient depth to to practically applied. Well presented for an overview of statistical terminology.

par Thodoris S

May 23, 2018

too much overlap with Jeff's course in introduction to genomic data science

par Gonzalo C S

Apr 04, 2017

Bad or superficial explanations. The instructor speaks very fast and you need to continually stop the video to keep the pace. Some interesting commands and are shown, but the instructor seems to be tired of explaining them and defers explanations to lots of links at the end of each video.

par Andrew M

Oct 29, 2017

This course is the shotgun approach to this topic. There's way too much material covered so shallowly that the instructor may as well not have bothered. While it is true that the course is heavily annotated with web links and references, IMNSHO, this is a cop-out. This course could improve dramatically by extending it a couple of weeks and covering some of the material in greater depth. I think the instructor also also buried his lede by deferring the discussion of predictive statistics and an overview various experimental processes/software until week 4. Both of these topics deserve better treatment front and center in week 1.