Michael! Check http://eyechrom.com @ebsessa's undergrad student Rodrigo Rivero developed the shiny app. It was so much fun to do this with him.
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Very cool! Data viz projects seem like a good way to start: develop programming skills, learn the datasets, fuel curiosity as to what causes variation/outliers, sci comm, etc.
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simulation is always a good one
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Yeah, I agree, especially if you can get teach them to debug their code with model-based reasoning -- e.g. "if rate=10 and time=10, then why don't I see a mean of 100 events across replicates?". I've noticed some undergrads find learning code + stats to be overwhelming, though.
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I'm still trying to figure this question out myself, so I'd be VERY happy to brainstorm. So far I've found writing file parsing scripts is a month-long exercise, but this also reflects how awful the programming courses at my uni are. I am trying to fix that problem, but admin...
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File parsing is a great task, especially if it's to write a reformatted dataset. You can provide a mock-up output file that students must generate with their script.
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Also fwiw becuase i'm super shameless, my pyvolve package is REALLY intuitive for students doing "plain" phylogenetic sequence simulations https://github.com/sjspielman/pyvolve …. I've had one student doing simulations, and it's only 4-5 lines of code to get it done so he's been ok!
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Compare the results of different methods on (our) public datasets; plot selected outputs of bioinformatics tools; assess the relationship between a phenotype and gene expression; characterize a how unusual an outlier I came across is. 1/2
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Evaluate a promising R package with vignette and then with our data; document the negative consequences of comparing across incompatible datasets. Undergrads are great!! 2/2
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