Reorder syllabus

[k8hertweck]

Based on responses from last quarter, it looks like the following would be a preferable order for material to be presented:

intro to class
command line
git
python
R tidyverse
R genomics
synthesis

This will require:

• reformatting the syllabus
• reordering lectures
• updating README and lecture files

[k8hertweck]

Lecture schedule has been updated: https://github.com/fredhutchio/tfcb_2020/blob/master/README.md#class-schedule

Dates for these lectures are correct, and links are to copies of last year's materials.

[rasi]

@k8hertweck I wonder whether we need remote computing lectures at all. What if we just have two classes each for course summary and synthesis? Or perhaps @gavinha can expand his lectures to one more? He can also teach using command line tools this time instead of Bioconductor if he prefers that.

[k8hertweck]

I think it would be good to expand the genomics section of the course to represent more authentic workflows. I'm not sure about the exact flow of material. The main considerations are:

• genomic data are cognitively complex to understand
• genomics workflows integrate across different tool types
• students dislike splitting classes into lecture and lab: they want activities interspersed with explanations
• Jesse covered plotnine last year, which essentially implements ggplot code in python. I wouldn't recommend covering plotnine if we aren't introducing ggplot first; we might as well use matplotlib or seaborn for plotting in that section (since those were also covered last time)
• Jesse also covered working with sequencing data in python, but did a good job explaining relevant pieces of information about fastq files and whatnot so I think this will still work alright?

I'm inclined to think it would work best as follows, with lecture/activities interleaved:

• Rasi: intro to R/tidyverse/data viz (since R is the last main tool to be introduced, it seems wise to do that as early in the course as possible; 3 classes)
• Gavin: intro to genomic data and tools for sequencing data (from last year's lecture), including command line tools for genomics (2 classes)
• Gavin: genomic variant analysis (from last year's lecture) and genomic analysis in R (trimmed down from last year's R notebooks, 2 classes)

I'm happy to work with Gavin to repackage the material and add in the command line sections.

This approach would effectively present a complete genomics workflow. We wouldn't have time to do an additional synthesis at the end, but I think it would still work alright?