A Practical Computing Course

This post originally appeared on the Software Carpentry website.

While it was not a SWC course, this summer Casey Dunn and I taught a 12-day Practical Computing summer class at Friday Harbor Labs. It was a great group of students—mostly somewhat beginner level—and we covered regular expressions, the shell, Python, R + ggplot, Git, graphics, and bit of electronics. The centerpiece (which the students really got into) was a personal project that was applicable to their own interests and which they solved with some combination of the tools we covered. Many of the students went from zero experience to having a script that actually gave them insight into the real-life research back home.

The part that might be most relevant to Software Carpentry is that we just sent out a two-month-later follow-up survey to figure out what stuck and what didn't. I collated the responses, which I have summarized at http://practicalcomputing.org/fhl-feedback-2015.

I found the results really gratifying. Given two weeks of their full attention makes a big difference in how effectively you can introduce and reinforce the lessons, and overcome mental blocks (dictionaries, for some!). There was also a lot of peer-teaching going on after the seven or so hours of planned lectures and exercises. Students would work into the night on their projects and teach and learn from their classmates.

We told them at the beginning to start thinking about a project that might be relevant to their work, and that would benefit from automation. Then after a week of the class, when they had passing familiarity with the tool set, we had them discuss in class what they were thinking of trying. It ranged from using curl to scrape images from a museum web page for the plants they studied, to a teaching tool for simulating DNA replication and transcription, importing, and plotting data in R. There were a lot of variations on "search for a motif in a set of DNA sequences", etc.

There was a lot of consultation with instructors and other students. We had them each create a Git repo on BitBucket so we could review their code and create issues or make comments. (There are better ways to manage this that have since come to our attention.)

On the last day they each gave a presentation or demo of the state of their project, whether totally finished or not, and it was amazing! Projects included:

  • A student who had previous Perl experience but not Python created a pipeline for searching for toxin genes within a transcriptome. This required generating queries from a large toxins database and then searching for each of these queries in a large transcriptome database. He is going to continue working on it and publish this tool.

  • Create an R Markdown file with embedded ggplots for distribution data of lobster populations.

  • Scrape a botanical museum web page, and within a table of holdings, find names and barcode numbers for all images of mangoes, then download all the raw images and save the files with the name of the species as the file name.

  • Write an R script to find annotations in a video database where the depth and time are within a certain interval, flag them as potential duplicates, and export the decimated data set.

  • Collate records from two different databases (presented as delimited text files) and find the corresponding entries between the two.

  • Using a sliding window, calculate a weighted similarity score for two animo acid sequences and generate a colorized HTML file showing the location of various kinds of matches.

During the class we spent a lot of time coming up with canned exercises and debugging tests to drive home certain points, but they were really motivated by this chance to make something personally useful right away. This worked because we had them for 12 days; it would be harder to achieve in two or three.

The best anecdote about the impact it could have was from a professor who studies genes that bind to the end of microtubules as they grow during cellular processes. There is a particular motif which characterizes this family of proteins and it has the characters something like S, (L or V), T, (L or T). Their lab also had another suite of proteins which they thought might be involved in this process of microtubule elongation, but were planning all kinds of complicated tests, including transgenic mice, to determine whether it really was in this family. With a simple Python script, she was able to search for the regex S[LV]T[LT] among these suspect sequences, and lo and behold, out popped a couple of matches. She had never programmed before, and was extremely excited about these real-world results.

As for demographics, the class had 8 women and 6 men. 10 were on Macs and 4 on Windows (one of whom worked remotely on a Unix server, and one of whom bought a Mac when they got home after the course.)

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