When we think of statistics, it has a nasty tendency to stir up school based maths trauma, which can make us feel dumb and slow. It conjures the spectre of monstrously complicated equations, low marks on maths tests, and ideas we can't even imagine being able to understand.
Data Science is incredibly powerful, and there is a lot of insight to be gained using basic spreadsheeting and graphing skills. It's within your reach. What might you do with it?
Assessment is all down to this simple number. Objectivity guaranteed. But if there is a correlation between socioeconomic status… if girls are driven out of particular subjects by the perception that they are not suited to them… if rural kids don’t have access to the same range of subjects… if some schools don’t have great teachers or support structures… then what we have is the pretence of objectivity and fairness, rather than actual objectivity and fairness.
It doesn’t matter what technology you teach, when you’re teaching Data Science. I don’t care whether you use Python, R, spreadsheets, or stacking blocks to make graphs and analyse your data. What matters, above all else, is that you teach your students to ask critical questions about the data. How was it collected? What are the definitions you used? How do we know the definitions are valid? What other definitions could we use, and how would that change the data?
An amazing conversation with Neuroscientist Associate Professor Nic Price from Monash University, who has a lot to say about the way we teach science, how we can understand the brain, and how we need to get comfortable with uncertainty. Check it out!