Modelling Mindsets is a wonderful book that places the spotlight on a seldom-discussed aspect of data science: it is not a book about how to do modelling, rather, it is about meta-modelling, introducing and contrasting the various approaches to data science.

In any data science problem, whether you realise it or not, you are implicitly using a modelling mindset (Frequentism, Bayesianism, Causal inference, Machine Learning). This book explains the philosophical assumptions used by each mindset and contrasts their applications. The ideal audience of this book is a reader with some experience in applied statistics – perhaps a physicist who has had to run some regressions, a product manager who has done some A/B testing, or a trader attempting to quantify the distribution of returns surrounding a trade – who may not even be aware that they are subscribing to a particular mindset.

I first got interested in data science via machine learning – like many other naive kiddos, I was attracted by glitzy random forests and SVMs and uninterested in boring classical statistics. It has taken me many years of trying different approaches to attain a fraction of the wisdom that Molnar lucidly conveys. While Modelling Mindsets is impractical in the sense that it does not teach you how to apply any of the mindsets, it is highly practical in the sense that it helps you reason about which approach to use for a certain problem. It’s pitched at an ideal level of abstraction, condensing entire fields of academic literature into several sentences explaining the purpose and context for a particular approach, if not the how-tos.

Modelling Mindsets is an excellent tour of the toolbox for data science and an important read if you want to be someone who uses the right tool for the job.