I have taken a number of Massive open online course (MOOC) since 2014 and here is a short summary.
In general, I believe learning-by-doing is the most efficient way, and learning a subject without applying the knowledge to solve any practical problem is unlikely resulting in a good understanding. That being said, I'm kind of opposed to learn things (I feel) which are less relevant to my current work, for example, I used to think it doesn’t make much sense to learn NGS analysis if I am not doing NGS study. However there is a dilemma that, in many circumstances, when facing a complex problem, you need a certain level of skill/knowledge and be aware of existing tools available to use. Through this personal learning experience, I could say I benefited quite a lot from the MOOC and I am happy that I invested my time in learning. These MOOC courses serve as a good staring point to build a broad knowledge base.
The most popular MOOC websites are Coursera, EdX, Stanford OpenEdX. The former two provide more courses on genetics/genomics/bioinforamatics; and in OpenEdX, some courses are not free.
At Coursera, I obtained a certificate from courses including:
Johns Hopkins University Regression Models
Johns Hopkins University Bioinformatics: Life Sciences on Your Computer
Johns Hopkins University Statistical Inference
Johns Hopkins University The Data Scientist’s Toolbox
Johns Hopkins University Reproducible Research
Johns Hopkins University R Programming (highly recommended)
University of Michigan Programming for Everybody (Python)
At EdX, the courses I finished with a certification:
HarvardX - PH525x Data Analysis for Genomics (highly recommended)
MITx - 6.00.1x Introduction to Computer Science and Programming Using Python (highly recommended)
MITx - 7.QBWx Quantitative Biology Workshop
At OpenEdX, I took the course Statistics in Medicine but didn’t finish it.
The courses I like the most are:
this course covers almost a wide range of the genomics analysis and it is easy to follow the instruction. And you can always find something useful. From this course, I got to know the pheatmap , a handy tool to plot (elegant) heatmap, and now it becomes one of my favourite R package.
very entertaining and I like the way how Prof Crimson taught. It is not only about how to program with Python, I learned more on computational thinking.
R Programming - I skipped a lot lecture videos but enjoyed working on the assignments
The Bioinformatics: Life Sciences on Your Computer and Programming for Everybody (Python) were somehow too basic and not challenging enough.
The Quantitative Biology Workshop (7.QBWx), in my opinion, is not very focused and the transition from the learning material to the question is not always smooth. Although I understand that Matlab is widely used in the field of neuroscience, I do hope the course can adopt R or Octave over Matlab as the former two are free software.
