Algorithms for DNA Sequencing

Hello! I am a developer who's considering moving into bioinformatics. I took the course to get an understanding of the type of problems that computer scientists face in bio. Wanted to give you some feedback.

Pros
1:) Good introduction to the problems faced by computer scientists in bioinformatics. Got me very interested in the topic.
2:) Described the right amount of biology: enough to understand problem, but definitely geared towards algorithm side.
3:) Actual description of the algorithms and their complexity was pretty clear.
4:) Practical slant with lots of actual coding.
5:) Pace was good and Ben is talented an explaining concepts.

Cons
1:) Would have been nice to use some of the tools bioinformaticians use. I understand that we're learning from first principle a lot of the algorithms that they actually use in their tools, but it would have been nice to learn, say "Boyer Moore is used in such and such tools whereas kmer indexing is used in such and such" and then actually tried them out.
2:) Along with that, if we had a meatier project than just the programming assignments we were given, one closer to what actual bioinformaticians do, that would also have bee nice.

Of course, both of those cons are perhaps outside the scope of the class, so I understand why they weren't included. Overall, very worthwhile class, 4.5 stars out of 5 (rounding up to 5).

This course deals with the algorithms employed by mapping and genome assembly programs commonly used. It was only after completing the course that I realised that the course instructor, Ben Langmead, is actually the first author of the bowtie paper which is one of the most commonly used programs for DNA mapping. The lecture material is extremely well explained and accessible both to students with a computational background and to biologists. The algorithms employed are much better explained than in some other bioinformatics courses on Coursera that deal with some similar topic (UCSD Specialisation, etc) as the course specifically deals with the algorithms rather than teaching bioinformatics in general (though I find the other courses quite good too). The Practical courses are extremely useful, and help a great deal to understand how the algorithms described during the lectures can be easily applied in an accessible programming language. The Practicals are carried out in Python. Prior knowledge of Python is definitely a very big plus to getting the most out of this course, but not essential to understanding. I really recommend this course and really hope to see more courses from the same instructor in the future!

I feel that I have been finally introduced to the real world problems, their potential solutions and the paths for the improvement of such solutions. I feel that I've learned a lot about implementing the knowledge I've acquired so far and not only the knowledge gained through the specialization courses I've completed, and, more importantly, I feel myself much more drawn to the field of computational molecular biology and genomics than I ever was before. The only thing the course is missing is more of the same. I highly recommend the course for anyone interested in making first steps in serious DNA analysis and processing. Dr. Ben Langmead, along with lecturer Jacob Pritt, has done a magnificent job at making this course engaging, progressive and fun.
My background is predominantly in genetics and molecular biology. I also had relevant programming experience in Python and would recommend anyone who plan on taking the course to acquire the same. Any other programming language would probably be fine but the language of choice for the course is Python.

I have taken six other courses in this JHU Genomics series on Coursera and many others in Data Science @ JHU, Cybersecurity @ the U of Maryland etc on Coursera. I think this module is one of the very best I have taken. The video lectures with theory, explanations, and the implementation practicals are excellent. Just the right length lecture modules with crisp explanations and examples. The Quiz and Programming HW assignment content and approach reinforces making sure one understands the key concepts. The learning/frustration quotient on this class is very high. The Python HW programming assignments have all been challenging and fun. The final one is terrific and getting the virus back from BLAST was a blast - giving one a taste of the excitement of scientific discovery.

This course has a serious problem for anyone who has only taken the previous Introduction to Python course. This course fails to cover how to install and use the Python IDE (e.g. Anaconda, Jupyter, Notebook). The instructors just go ahead and present material in Notebook without showing how to load it, creating, or edit it. They were unable to explain the errors and grayed-out controls that I asked them about. They are unresponsive to questions. Answers were often variations on "you're really close" which is pointless. They do not seem to know how they got their own tools set up and now are unable to state a simple series of steps and configuration settings. Consequently, I come away feeling that the Python language and tool suite is not suitable for real-world work. It is a patchy academic introductory teaching tool at best.