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Learner Reviews & Feedback for Statistics for Genomic Data Science by Johns Hopkins University

4.1
196 ratings
37 reviews

About the Course

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....

Top reviews

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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26 - 34 of 34 Reviews for Statistics for Genomic Data Science

By 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

By sandeep s

Dec 20, 2016

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

By John M

May 25, 2017

Covers a large amount of material in a short time.

You will learn a lot but you will have to spend a lot of time researching and experimenting.

By Thodoris S

May 23, 2018

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

By David B

Feb 24, 2019

Theory part, remaining that it has to be done in pills, could be done a lot better. R part is done better, but the principal issue is that you have not a clear connection with theory.

By Matt C

Jun 27, 2017

For some reason, this was a really tough course, it blew my socks off. I did not get the explanations they just did not sink in.

By 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.

By 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.

By Hylke C D

Sep 25, 2019

Much of the code is broken because it is outdated. In the specialisation you learn to use Python, and here all of a sudden they switch to R. Some familiarity with R is assumed in this course. A lot of the functions and packages that are used are not discussed at all. By far the worst course I have taken on coursera so far.