I am torn about this class. On one hand, there was some good information, and I definitely learned more throughout the course. On the other hand, as a biologist trying to learn programming, this course was insufficient. Very little of the programming code was explained (and considered obvious?). I spent a lot of time googling to understand the code so that I could not only learn but complete the assignments. There are some concepts (like the use of __init__) which I still do not understand. Also, the "hints" to accomplish the homework assignments were definitely not well explained. I only managed to increase the speed of my programs by going through the discussion forums for hours until I found something that helped.

Also, as a biologist, it is obvious that this course is not designed so that a computer programmer learns much about the biology. A simple (but necessary!!) change would be to refer to the left and right as 5' and 3'. Using biological terminology would have helped me understand what the lecturer was talking about.

Plainly, this is a course for a computer person to learn more code. It could be taught to do so much more!!

I feel this course is getting old, mainly the parts that use R. R software /modules are used are outdated and not supported by latest R versions. There is no instruction as to how to make the software work,or exactly which older R version supports what is required to get classes 6 and 7 done. + the virtual box recommended for Course 5 the command line course is not working under Windows 10. There is no info as to if Windows 7 is required. + It appears there is no support either. I have loaded linux on my window 10. Will try coursera 5 one more time. The problem with this program is it takes way, way too much time to get the software to work. And coursera and the teachers do not seem to have response at all.

The subject of the course is very interesting. Lectures are very engaging and professors do a great job explaining abstract concepts. On the other hand, assignments are extremely difficult and frustrating. The lack of feedback for the assignments leaves you with important knowledge gaps and that's a serious flaw for a course that offers so much most of the time.

This was really fun. Really enjoyed the a-ha of the algorithms and the fun of solving the alignment and assembly problems. Feel mildly powerful after assembling a virus genome.

The homework is confusing and would cost a lot of time on unrelated stuffs.

This course was a really awesome course for anyone that has a light background in Python and wants to look more into bioinformatics. The instructors were very passionate and clear, and there was a good balance between learning about biology and the programming aspect. However, for somebody that only has the programming knowledge from Course 3 of the Genomic Data Science Specialization (Python for Genomic Data Science), that course, I believe, is too light for the new Python concepts taught in this course. I believe that before taking this course, you may need more background knowledge for Python. However, this is still a stellar course that I greatly enjoyed, with the homework and assignments being appropriately challenging.

Excellent intro to the computational challenges in analyzing genomic sequences.

Lectures and programming exercises explain algorithms very clearly even for beginners.

good course but no support available at all...

This is an excellent course. Lectures are very well prepared, practicals provide step-by-step explanations of the scripts (which is especially useful for people with little coding experience) and homeworks are well thought through, so that they force students to use the knowledge gained in the module. Some of the homeworks are challenging, but all the information needed to do the exercises is provided in lectures and practicals. All the notebooks containing scripts are provided, which makes it easy to take notes and better understand the scripts by running some examples. The way the concepts are explained in the lectures (the computational problem is described in details and then the ways of dealing with it are carefully explained in order of increasing complexity) provides insight into not only how these algorithms work but also why (what is the purpose/cause/reason behind these solutions). I can imagine how much work and thought went into preparation of these lectures and I honestly admire the teachers for their efforts. Taking this course was a great experience: I learned a lot and enjoyed it a lot. A big thank you! Please, keep up the good work.

This is hands-down the best course in this specialization. There are good teachers (many of whom are teaching the other courses) and then there are the top 20% excellent teachers (Ben Langmead) who teaches this class. In addition to being an excellent computer scientist, he is also an excellent teacher and he makes the material interesting and approachable. The homework assignments are challenging, but created in such a way that the difficulty lies more in grappling with the concepts themselves, and less with debugging python code or whatever. I highly recommend this course, and hope that Ben will do a "Algorithms: Part Two" covering Burrows-Wheeler and going into more depth, maybe discussing pseudo-alignment as well.

I would definitely like to thank Ben and Jacob for this amazing course. I love the way Ben manages to present and break down the problems in a very concise and easy to follow way. The practicals were very useful to understand the code. I do admit (as was written in the first week) that an undergraduate degree in computer science would have been a great help. I struggled quite a bit as I had to learn programming from scratch but I still was able to learn a great deal about the bioinformatics world. Thank you once again

Very good course! You end up with a repertoire of nice algorithms that might be useful in many kinds of genomic analysis. While these are far from the optimal algorithms used by the actual genomic programs, they provide a nice introduction to the subject. It would be very good to have another course for "Advanced algorithms for DNA sequencing" where strategies for dealing with the several complicators of actual genomic data are added on to the knowledge obtained in this course.

I'm just under halfway through the course, but am so impressed with the content and the presentation that I had to give this a 5-star rating. The instructors are great, the practical walkthroughs are great, and the homework assignments do a good job of testing you on the content and your ability to apply the knowledge to new situations.

Very good course this is. Just a little advice, please make the quiz questions more clear and more specific because one shouldn't waste time to understand a question which is very easy to implement.

very engaging and well-presented course material.

intermediate difficulty while conveying the basics of how even recent real-world genome alignment and assembly tools work. great course design!

Highly eloquent instructors that make intricate concepts accessible and fun to learn. Would definitely help if you have some programming or bioinformatics background beforehand.

One of the most useful courses I have joined.

Many thanks to the instructors for these quality lectures and for the time spent to deliver the information as easily as possible!

Great course! Content very well selected and presented. Instructors put a lot of effort in creating the course material. It was challenging but definitely woth my time.

A wonderful course! It's a little bit challenging but really informative! And the explanations are detailed and friendly for beginners!

very deep knowledge... if anyone wants to learn how to analyze DNA sequences with Python. it is the place to be.

Very well done class. The lecture will give you a easy to understand explanation of all the principal thought.

Very detailed demonstration of practicals. Would recommend for beginners.

Muy buen curso, bien explicado por los tutores.

The instructor was outstanding.

As a biologist with minor experience in bioinformatics (and coding in R), I found the contents of this course extremely well presented and conveyed in a very clear manner. The practicals were very helpful in understanding the code behind the algorithms, even though some more advanced code was not explained, which is fine. Kudos to both the instructors! I recommend this course to anyone who is working with NGS data and is interested to learn how tools like salmon or bowtie work in principle. Now, I finally learned how Fastq files are built and what information they carry and what a genome index is. 12 hours to complete is definitely an understatement, though. The homeworks are challenging and give you the opportunity to hone your Python and computational thinking skills. Too bad, this course is not supported or updated any more. I hope there will be a revamp at some point.