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Avis et commentaires pour l'étudiant pour Statistics for Genomic Data Science par Université Johns-Hopkins

4.1
185 notes
35 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!

LR

May 23, 2016

I have really enjoyed the course and I have learnt different concepts relevant for my current study.\n\nYurany

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1 - 25 sur 32 Examens pour Statistics for Genomic Data Science

par Ian P

Aug 30, 2018

I did my best to work through module 1, but encountered one problem after another with installing the various required R packages, due to version issues. From the absence of recent discussion posts it seems that this is not really a current, viable course. From what I have seen of the course, I get the impression that even if package installation went smoothly, the course is more about R than statistics or genomics - which is not what I joined for.

par Hamzeh M T

Nov 08, 2018

Great place to start learning genomics in R

par ELISA W

Jul 23, 2018

I think this is one of the best courses in this specialization. I found it the most helpful in building together what should be learned in genomic data science. I wish 1) this course was earlier in the specialization, 2) there was additional building from this course to build together the workflow from beginning to end, and 3) reduction or removal of some of the other courses (or other courses taught together with this one).

par Chunyu Z

Feb 10, 2016

very helpful class. instructor very organized.

par Gregorio A A P

Aug 26, 2017

Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

par Juan J S G

Mar 07, 2017

La semana 3 puede hacerse dura, pero el curso es muy completo y recomendable.

par Maximo R

Mar 22, 2016

Great course!!!!

par Apostolos Z

Oct 21, 2017

Excellent course! Thank you!

par Roman S

Jan 04, 2018

Really great and in-depth class! thank you

par Alex Z

Aug 07, 2017

talk fast and informative! I enjoyed it a lot.

par 李仕廷

Jul 01, 2018

really a good course for people who want to learn use R to dispose genomic data

par Zhen M

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!

par Luz Y M R

May 23, 2016

I have really enjoyed the course and I have learnt different concepts relevant for my current study.

Yurany

par Tushar K

Mar 25, 2019

Very good course and useful understanding statistical aspects of data.

par Chuan J

Jul 16, 2019

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

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 Dr. P R I

Mar 01, 2019

good

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 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 Michael R D

Feb 12, 2017

Nice course. Ready to apply data.

par NAMRATA P

Apr 10, 2019

good

par Ariful l

Aug 12, 2019

good for learning

par Hemanoel P A

Jan 24, 2019

This is totally not for beginners..

par Stefanie M

Feb 25, 2019

In the course, easy concepts are well explained, but the more complex topics are very tricky to understand. However, I appreciated the enthusiasm of the teacher a lot

par sandeep s

Dec 20, 2016

The course was tough and was explained in a very fast way assuming that the student knows prior statistics.