Software Carpentry @ Arizona will provide training in computational skills for scientists. The instructors will be Titus Brown, Karen Cranston, and Rich Enbody. While scientists care about correctness, reproducibility, and efficiency, efficiency is often prioritized over correctness and reproducibility. As computational methods become more prevalent in science, this could lead to more incorrect findings going undetected. The training aims to help scientists become more efficient and effective in their computational work while also improving correctness and reproducibility through best practices like automation and version control. The sessions will focus on Python but will discuss its relationship to R and how multiple languages can be learned.

1. Software Carpentry @ Arizona!

2. Instructors
• Titus Brown
• Karen Cranston
• Rich Enbody
• Deren, Chas, Katie, Nirav

3. What do scientists care about?
1. Correctness
2. Reproducibility and provenance
3. Efficiency

4. What do scientists actually care
about?
1. Efficiency
2. Correctness
3. Reproducibility and provenance

5. Our concern
• As we become more reliant on computational
inference, does more of our science become wrong?
• “Big Data” increasingly requires sophisticated
computational pipelines…
• We know that simple computational errors have gone
undetected for many years
– a sign error => retraction of 3 Science, 1 Nature, 1 PNAS
– Rejection of grants, publications!
http://boscoh.com/protein/a-sign-a-flipped-structure-
and-a-scientific-flameout-of-epic-proportions

6. Our central thesis
With only a little bit of training and effort,
• Computational scientists can become more
efficient and effective at getting their work
done,
• while considerably improving correctness and
reproducibility of their code.

7. Automation

8. Why Python, and not R?
In my opinion,
• Python is a more general purpose language, while R is
mostly about data analysis.
• Everyone will need to learn multiple languages; R and
Python are pretty dominant in bio right now.
• Luckily, once you get the hang of it, new languages are not
so difficult to pick up.
• Ultimately, we’re trying to teach process not details.

9. Administrivia
• Asking for help
• Using the Web site
• Sticky notes: ok? Not ok?
• Minute cards: at the end of every session, write
down
• One thing you learned
• One thing you are confused about