A Reading List Curated for Perspective
There exists no shortage of Data Science & Analytics books for teaching technical knowledge, yet few for teaching practitioners how to think critically about the problems they are solving and understanding the implications of their work.
Gnome Press
(1951)
FOUNDATION Trilogy by ISAAC Asimov
Nothing else comes close when exploring the possibilities and limitations of statistical science applied to social systems. Asimov's seminal work is, at once, an inspiring and cautionary tale.
It also features literature's greatest (perhaps only) statistician protagonist!
Random House
(2001)
FOOLED BY RANDOMNESS by Nassim Nicholas Taleb
This book is a deathblow to the overconfidence that pervades Data Science. As humans, we overestimate causality and tend to view the world as more explicable than it really is. Our behaviors deviate from 'rational choice theory' precisely because we are so poor at assessing the world around us by ascribing success to skill and failure to chance. Only when we truly understand the role randomness plays in our lives can we build insightful and functional models and make better choices as an individual.
Penguin (2012)
The signal and the Noise by nate silver
Nate is perhaps the only celebrity statistician we are likely see in our lifetime (reason enough for excitement) but his message is also a powerful one: go back and check your work. How often do we build models and make forecasts that we never revisit? How committed are we to actually explaining phenomena over time and not just answering the question of the day? How deeply can we seek the truth?
Anchor
(2004)
The wisdom of crowds by james Surowecki
Diversity. Independence. Decentralization. Aggregation. The four requirements necessary for a crowd to be wise, co-incidentally also the four requirements for a large set of data to be valid. It's easy to forget the data we are analyzing originates from people and if that data isn't collected the right way or contains inherent biases, the results of our analysis are going to be as un-wise as the crowd from which they were drawn.
Farrar Straus Giroux (2003)
CRITICAL MASS by pHILIP Ball
A fascinating read on the confluence of models that explain both social and physical systems. Cross-pollination of ideas between disciplines is the largest driver of innovation, and here Ball connects mathematical models from Physics, Biology, Chemistry, Economics, and Sociology. He also explores the idea of 'step-changes', where systems behave in entirely different ways after some threshold is crossed. I've used this approach many times to reconcile models which were valid for one set of data and then inexplicably invalid for another.