Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. I recently gave a talk where I argued the importance of asking Why, touching on three different topics: stakeholder motives, cause-and-effect relationships, and finding a sense of purpose. A video of the talk is available below. Unfortunately, the videographer mostly focused on me pacing rather than on the screen, but you can check out the slides here (note that you need to use both the left/right and up/down arrows to see all the slides).
If you’re interested in the topics covered in the talk, here are a few posts you should read.
Stakeholders and their motives
- If you don’t pay attention, data can drive you off a cliff
- The hardest parts of data science
- You don’t need a data scientist (yet)
Causality and experimentation
- Making Bayesian A/B testing more accessible
- Diving deeper into causality: Pearl, Kleinberg, Hill, and untested assumptions
- Why you should stop worrying about deep learning and deepen your understanding of causality instead
Purpose, ethics, and my personal path
- Should data science really do that? (on KDNuggets)
- The long road to a lifestyle business
- My divestment from fossil fuels
- The rise of greedy robots
Cover image: Why by Ksayer