Bioconductor for Genomic Data Science

The Bioconductor course is technically sixth in the Genomic Data Science specialization, although I skipped directly to this after taking the third course covering Python. The course covers a bit of basic R programming (although experience with R at the level of R Programming is almost a necessity unless you have previous programming experience and are able to pick up the language very quickly. The course then dives right in to many of the most common Bioconductor packages for genomic data analysis, including GenomicRanges, AnnotationHub, Biostrings, ExpressionSets, and others. The final week includes an overview of getting your data into Bioconductor, as well as some advanced packages.

I found this to be a very good course. I had previously taken the EdX Introduction to Bioconductor course, so I had some experience with most of these constructs, but I still found this course to be an almost perfect balance between challenging and achievable. There were some technical issues along the way, including compatibility issues due to the new release (at the time) of Windows 10, but Dr. Hansen was very involved on the discussion board, and most of these were resolved relatively quickly. Bioconductor packages also undergo frequent updates, and this often can result in the same code giving different answers across versions, so it is critical to use the same Bioconductor version that was used in designing the quizzes, even though it will likely be far out-of-date by the time you take the course.

Overall, four stars. I enjoyed this course more than any others in the specialization so far, and for someone like me coming in with some experience with R, it had just the right amount of challenge. Dr. Hansen's presence in the course forums also enhanced the course, especially since there were no community TAs at the time to answer questions, despite it being apparently the fourth iteration of the course when I took it.