I attended a great webinar by Alex Reinhart, From Code to Products - Software Engineering for Data Science. He is part of the Delphi Group at Carnegie Mellon University, a research group working on models of infectious diseases. His last slide had additional resources, which I list below. I’ve only skimmed a few of these so far, but everything I’ve seen looks really good.
Resources
- Catherine Nelson (2024). Software Engineering for Data Scientists. O’Reilly. Publisher’s webpage
- Alex Reinhart and Christopher R. Genovese (2021). “Expanding the scope of statistical computing: Training statisticians to be software engineers.” Journal of Statistics and Data Science Education 29 (S1), S7-S15. doi:10.1080/10691898.2020.1845109
- Fiksel, Jager, Hardin & Taub (2019). “Using GitHub Classroom to teach statistics.” Journal of Statistics and Data Science Education 27 (2), 110-119. doi:10.1080/10691898.2019.1617089
- Hadley Wickham and Jennifer Bryan (2023). R Packages, 2nd ed. O’Reilly. Free version
- Steve McConnell (2004), Code Complete, 2nd ed. Microsoft Press. Publisher’s website
- Scott Chacon and Ben Straub (2014). Pro Git, 2nd ed. Apress. Free version