On Tuesday, SSA Vic hosted a panel discussion on Statistics education in the age of Big Data. One of the panellists was Julie Simpson, who I work with at ViCBiostat. She decided to poll the ViCBiostat postdocs beforehand to get our thoughts and channel them into the discussion.
I thought back to how I would change my undergraduate learning and came up with two suggestions:
End-to-end exposure on working with real problems. That means everything from planning an experiment or study, dealing with the acquisition and cleaning of the data, through to delivering a final report or presentation (or interactive web app…).
A mental map of statistical methods. That is, a broad understanding of all of the different areas of statistics (and machine learning, data mining, etc.), how they relate to each other, and what types of problems each of them are useful for. I think is more useful than learning to be highly proficient in a few methods and being ignorant of what else is out there (which accurately describes my state after undergrad, although it was even worse because I was too ignorant to appreciate how ignorant I was!).
Ideally, both of these would be slowly developed over the whole degree, but they can also be explicitly taught as part of a ‘capstone’ subject in the final year. A quick web search for ‘statistics capstone’ reveals that some universities (mostly in the USA) indeed seem to run subjects of this sort, especially focusing on the ‘end-to-end’ aspect. I don’t know if they also provide a mental map. If not, I think that would be a valuable addition.