Interpreting Models: Coefficients, Marginal Effects or Elasticities?

I’ve spoken about interpreting models before. I think that this is the most important part of our work, communicating results. However, it’s one that’s often overlooked when discussing the how-to of data science. That’s why marginal effects and elasticities are better for this purpose than coefficients alone. Model build, selection and testing is complex and nuanced. … Read more…

Elasticity and Marginal Effects: Two Key Concepts

One of the critical parts of building a great model is using your understanding of the problem and context. Choosing an appropriate model type and deciding on appropriate features/variables to explore based on this information is critical. The two key concepts of elasticity and marginal effects are fundamental to an economic understanding of model building. This is … Read more…

Machine Learning vs Econometric Modelling: Which One?

Renee from Becoming a Data Scientist asked Twitter which basic issues were hard to understand in data science. It generated a great thread with lots of interesting perspectives you can find here. My opinion is that the most difficult to understand concept has nothing to do with the technical aspects of data science. The choice of when to … Read more…

Tutorials and Guides: A curated list

This post is a curated list of my favourite tutorials and guides because “that one where Hadley Wickham was talking about cupcakes” isn’t the most effective search term. You can find my list of cheat sheets here. There are a lot of great resources on data science (I’ve included my top picks), so I don’t intend … Read more…