Python for Genomic Data Science

This is the third course in the Genomic Data Science specialization. I have mixed feelings about this course. Much of the lecture material and the quizzes are devoted to introducing students to the Python programming language. Python is used commonly in bioinformatics due to its simple syntax and the wealth of packages (e.g. Biopython, NumPy) available for data processing and genomics. There's very little in the course material that will assist students in actually learning to code prior to the final exam, as most of the quiz questions simply ask students to determine which given lines of code will produce the desired output.

The final exam then throws students into the deep end. For those without significant Python coding experience or years of experience in another language, the final exam will be somewhere between extremely difficult and impossible. There is a serious loophole in the final exam that allows students to score highly without doing any coding, however. As with all the other classes in the JHU specializations, students are allowed 3 attempts at the exam, and the exam itself is multiple choice with 4 answers per question. Additionally, the instructors have coded more than 4 answers for each question, so incorrect answers cycle in and out of the potential answer bank between multiple tests. This means that students who take the test 3 times are often presented with a situation where only 1 or 2 of the 4 choices shows up on all 3 attempts, making it easy to determine which is the right answer if you keep your previous attempts open while taking the third exam and carefully note your previous wrong answers and the choices which appear in all 3 tests.

My minimal coding experience and careful use of Biopython was enough to allow me to to answer the first 3 of 10 questions on the exam, but I scored 5/10, 8/10, and finally 10/10 using the strategy above in my 3 tries.

Overall, I'm giving this course two stars, the same as the Galaxy course. There must be a better way to introduce students to these topics than what the instructors have done in these courses so far. There has been too much of a disconnect between assumed knowledge and expectations in these first two courses (I'm ignoring Intro to Genomic Technologies, which is another story altogether). This is true of the Data Science specialization to a certain extent as well, but the jump between lecture content and course project expectations is likely too wide for most students here, unless they come from a background where they happen to have a fair bit of training in whatever topic happens to be the focus of the course and/or final project.

Good course for learning (very) basic programming skills in Python, but horrible as a bioinformatics course. I think it was a mistake to gear it towards individuals with absolutely no programming experience, and them make it only 4 weeks long. The reviewer above me is absolutely correct, you really do not learn enough about programming to complete the final project. This course should be geared towards individuals with beginner/intermediate experience with programming, and that way you can focus MUCH more on Python-based bioinformatics tools such as PyMol and BioPython. If you're attempting to learn bioinformatics without knowing programming, you're not gonna make it. Take basic programming courses BEFORE starting any bioinformatics coursework.

As a seasoned Java programmer learning the basics of Python taught by this course was very easy. However, the course has very little useful material on genomics. The most important failure of the course is that it does not prepare the student for using Python in the Algorithms for DNA Sequencing course in this specialization. Specifically, the Algorithms course uses Anaconda and its IDE, Notebook as used on a Mac or UNIX environment. There is a significant learning curve for Notebook in the Algorithms course which the instructors seem to be oblivious to because they are happily using a MAC. They state that you can do the work on a PC (or non-MAC unix) but they fail to provide the setup procedures which should have been provided in this (Python ) course.
I rated this course as suitable for Intermediate programmer because it assumes that you know how to install the IDE on any platform, convert platform-specific source formats (know that it is required), know how to use "Notebook". You can get through this course without those tools but you are left unprepared for the next course in this specialization that requires those skills.
The course does not convince me that Python is a suitable language for genomics work.