The worst executed course I have taken in 36 years of post-graduate education.

1 The instructor speaks so fast it is difficult even for a native English speaker like myself to understand.

2. This course is only suitable as a review for people who are experts in the field already. Even if you know how to use Bioconductor and are familiar with programming in R, if you don't know the tools being used already the instruction in the course will not give enough information to be able to do the quizzes without a great deal of difficulty.

3. The examples presented are thrown out in a cursory fashion without enough detail about how the data is being set up or manipulated. Matrices are transformed and recombined with little explanation about why things are being done.

4. Although generalizing from material presented to new applications is a valid instructional approach, the instruction does not give the student enough information to do this and the instructor expects students to be able to figure out new algorithms from vague public domain documentation.

5. Although the instructor makes an impassioned plea for carefully thought out statistical test design, proper documentation of work flow, and appropriate use of p-values, he does not describe the interpretation of statistical tools presented. For example, tools for calculating thousands of principle components in seconds is given, but beyond showing clusters of dots on a graph may indicate a genetic cluster does not explain what the individual points in the PCA mean.

In summary, the tools presented are very powerful but are not well described. Extensive revision to the course is needed.

Much of the code is broken because it is outdated.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.

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.

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

Very good course and useful understanding statistical aspects of data.

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.

This is totally not for beginners..

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

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.

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.

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.

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.

Great course overall! Good at those aspects: 1. a comprehensive cover of key statistics used in genomic data analysis. I have some experiences in genomic data analysis. Taking this class offers me a quick overview of the underneath statistical skills, which helps me gain more understanding of the bioinformatics analysis I have been working on. 2. The course materials are well organized and easy to follow. The Professor is proficient at the materials and also fun. Another thing I like is that the codes in the class can still be run smoothly without any troubles, even though it has been a few years since the class recorded. 3. The class also provides with other materials for further study, which are helpful.

Just a few downsides, the quizzes are a bit difficult. I often spent 5-6 hours doing research to get it right. Also, the forum of the course is not active. I did not get response for my question. Overall, I have learned the topics I need from this class, and the learning experience was quite fun.

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).

This is the best. It opens my eye for genomic data analysis.

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.

First, I really appreciate the enthusiasm of the instructor. And the overall topics introduced in the course cover a large amount of current genomic research. But there is still a lot of space for improvement. The course itself is not suitable for beginners, since it requires proficient R skills and some knowledge in statistics. The ways of illustration on many definitions are unprofessional and shallow, so it sounds confusing at least for me, who majored in public health and has some experience in this field. And the course is overall short, but the instructor tried to introduce as much as knowledge possible, which makes every point somewhat unclear. By the way, the descriptions of several exercises in the quizzes really make people confused.

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

The topic and syllabus are very interesting! Sadly, this is the most disappointed statistics course I have ever learnt. Maybe, it works for those who have very very advanced background. His lecture is too fast, lack of detail, unclear explaination, redundant and unorganized handout. I've become more sick of studying statistics after struggling to complete just 2 modules from here :(

Overall, the course has great content, but I am not satisfied with the presentation. I will greatly recommend that the course should be reviewed and be presented more professionally. The issue I found with the presentation is that it is not engaging and not student-oriented. It is quite difficult to follow the demonstrations. There is huge room for improvement!!!!!

I did my best with the R platform because the downloads weren't working, I was downloading outside them upload files to make it works...

I mean con = url(URL) wasn't working in the platform

so no way to load(file=con) command; But I did after downloading it on the R platform I install it locally with worries as know from my background that regression and methodologies used needed for performance than just a personal computer.

Thanks for this great problem-solving and challenging moment, I look forward to the next step in my admission process with the John Hopkins University on Bioinformatic or Bioengineering for more insight.

Thanks and I will highly appreciate you recommendation on the application.

My emails : angossiol.rayh@icloud.com or raya1@arizona.edu

The instructor is one of the best whose teaching and course design appealed to me most effectively due to his capabilities of being able to focus on the key issues even in a very broad area of knowledge and go right into them and make them understood sufficiently regardless of one's varied background. This is by no means an easy task. Jeff has a great insight in designing problems from learners' perspectives to maximize the learning experiences.

The course teaches useful materials in a clear way. I took it (inside GDS specialization) quickly because I have some biostatistics background, but I sugest to enjoy it with time!!

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!

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