15 May 2016 on personal and reading. 5 minutes

A running list of books I’ve enjoyed, and a few quick thoughts on what I found worth sharing.

Data Science

Introduction to Statistical Learning — James, Tibshirani, and Hastie

A solid overview of the statistical learning theory that underlies machine learning. Allows a reader to get an intuitive grasp of what is going on inside the “black box”, but is a little too far on qualitative side if one hopes to gain a full understanding. For a deeper dive, see the advanced version Elements of Statistical Learning.

Learning from Data – Abu-Mostafa and Magdon-Ismail

A reliable book on the machine learning theory, approaching problems from a theoretical rather than applied perspective. I would recommend this as a second book/course on machine learning, once the basics are understood.

Deep Learning – Goodfellow, Bengio and Courville

A wonderful balance of intuition and theory that the field has been lacking. Begins with the nuts and bolts of feedforward networks, and then goes into depth on the state of the art in model regularization, optimization, and various model classes and architectures. Not a light read, but highy recommended for an aspiring practitioner.

The Signal and the Noise — Nate Silver

My life would have turned out quite different had it not been for Nate Silver and this book. The Signal and the Noise helped me to discover my love for data science!

Big Data — Schonberger & Cukier

A high-level overview of the ways big data could change various aspects of government and enterprise, and both the risks and sources of value that are associated with this. Do not expect technical depth, but the book is a good first read for the curious.


Misbehaving — Richard Thaler

This book covers the rise of behavioral economics from one of its earliest practitioners. Thaler draws from his experience to give an often Freakonomics-esque run-down of how economic models fail to describe real-world human behavior.

Thinking, Fast and Slow — Daniel Kahneman

A layman’s version of the theories that laid the groundwork for behavioral economics. Kahneman explains the two chief mechanisms in our brains (fast and slow thought), and how they cause predictable biases.

Freakonomics I & II — Levitt and Dubner

My love for Steven Levitt’s work is second only to that for Nate Silver’s. Freakonomics showed me that the economics tool set can be used to advance causes much greater than economics itself. This has had a major impact on the way I have both applied and pushed the limits of my education as an economics student.

The Intelligent Investor — Benjamin Graham

The bible for any value investor. Graham’s Mr. Market illustration remains relevant today.

Peddling Prosperity — Paul Krugman

A breakdown of the dangers of journalist and pundit-fed pseudo-economics. Those who “peddle prosperity” in this way are seldom grounded in fact. This book breaks down the rise and farce of Reaganomics, and how to be weary of such false theories in the future.

What Money Can’t Buy — Michael Sandel

A book on incentives, and how just about everything has a price tag if framed correctly.

When to Rob a Bank and 131 More Warped Suggestions and Well-Intended Rants — Levitt and Dubner

A collection of posts from the Freakonomics blog strung together into a greater narrative. A nice mix of incentive schemes, economic ramblings, and musings on irrational behavior.

Misc. Applied Statistics

Superforecasting: the Art and Science of Prediction — Phillip Tetlock

An engaging read for statheads and trivia fanatics. Tetlock draws from his experience as the head of the Good Judgment Project (a lengthy study on forecasting) to break down what exactly makes a great forecaster able to see the future better than the rest of us. The key findings are based in psychology and methods of improvement through self-evaluation.

Moneyball — Michael Lewis

The story of how Billy Beane’s Oakland A’s are able to build successful teams in one of baseball’s smallest markets. Lewis’ walk through the logic of sabermetrics and sorting signal from noise in baseball data was eye opening as a stats geek and sports fan alike.


The Hard Thing About Hard Things — Ben Horowitz

Advice from a VC and former CEO about how to get through the low points as a leader. A master class on how to lead when things are not going well.

How Google Works — Eric Schmidt and Jonathan Rosenberg

A manual on how to build and scale an organization where innovation comes natural, told by two of the leaders responsible for doing this at Google.

Zero to One — Peter Thiel

Peter Thiel’s thoughts on how to start a startup. This book is filled with wisdom on market positioning, culture, and overcoming the challenges of early-stage entrepreneurship.

The Dhando Investor — Mohnish Pabrai

Simple, easy to follow value investing principles from a hedge fund manager.

Work Rules! — Laszlo Bock

How to fuel an organization through better human resources practices. Hiring, recruiting, and general people operations advice from Google’s SVP of People Ops.

The Little Book that Beats the Market — Joel Greenblatt

More value investing, this time greatly simplified. One of the most useful reads for an investor who is not a finance pro.


Lolita — Vladimir Nabokov

Nabokov has a beautiful writing style, making the book hard to put down. It begins with what is in my opinion one of the greatest (and creepiest, in context) opening paragraphs in all of literature:

“Lolita, light of my life, fire of my loins. My sin, my soul. Lo-lee-ta: the tip of the tongue taking a trip of three steps down the palate to tap, at three, on the teeth. Lo. Lee. Ta. She was Lo, plain Lo, in the morning, standing four feet ten in one sock. She was Lola in slacks. She was Dolly at school. She was Dolores on the dotted line. But in my arms she was always Lolita.”

1984 — George Orwell

One of my all time favorites.

A Portrait of the Artist as a Young Man — James Joyce

“Old father, old artificer, stand me now and ever in good stead.”

To the Lighthouse — Virginia Woolf

Another of my favorites on the literature side of things. Woolf’s style is fluid and poetic, bringing the epic into the world of thought and everyday life.


Building a Content-Based Recommender System for Books: Using Natural Language Processing to Understand Literary Preference

Literature is a tricky area for data science. Think of your five favorite books. What do they have in common? Some may share an author or genre, but besides that, it is probably hard for you to think of what traits they share. My team and I set out to explore the mysterious components of an individual’s literary taste profile, and in the process built a content-based recommender system for books. This post is a brief overview of the system, the features it uses, and how it was built.