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?
Tag: stem education
Paola Oliva Altamirano talks data for social good
This is a great chat about critical thinking and the importance of scepticism in Data Science, and the importance of data and scientific literacy around the world. Check it out!
Why doesn’t technology bring us great joy?
Whether hardware or software, smartphone or car, laptop or microwave? Have you ever looked at a piece of technology and thought "This is a gloriously beautiful thing that does exactly what I need it to do, and brings me great joy?"
Nic Price on Data, Neuroscience, and the brain
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!
Consider the impact
Real world problems don't have perfect, textbook answers. Real world solutions have downsides as well as upsides. And complex, multifaceted problems such as those we are trying to tackle with AI and Data Science are going to need complex, multifaceted, and carefully tested solutions.