The 10 best books on big data.

Looking for a Christmas present for yourself or your data minded partner? Have a look at this list

1. Everybody Lies, Seth Stephens-Davidowitz (2017)

Everybody lies, to friends, lovers, doctors, pollsters – and to themselves. In Internet searches, however, people confess their secrets – about sexless marriages, mental health problems, even racist views. Seth Stephens-Davidowitz, an economist and former Google data scientist, shows that this could just be the most important dataset ever collected

2. Predictive Analytics: the Power to Predict Who Will Click, Buy, Lie, or Die,Eric Siegel (2013)

You have been predicted by companies, governments, law enforcement, hospitals, and universities. Their computers say, “I knew you were going to do that!” These institutions are seizing upon the power to predict whether you′re going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.

3.   The Signal and the Noise. Why So Many Predictions Fail but Some Don’t, Nate Silver (2012)

Named one of the 100 Most Influential People of 2009 by Time, Nate Silver has raised to fame after correctly predicting the 2008 US election, and killing it in 2012 (by calling 50 out of 51 states). The Signal and the Noise is a deep dive into the world of applied probability. Challenging the traditional (“frequentist”) statistics, Silver makes a case for the complexity involved in event forecasting, supporting his observations with studies of poker, the financial crash, and climate change.

4.   Fooled by Randomness. The Hidden Role of Chance in Life and in the Markets, Nassim Nicholas Taleb (2005)

In the shadow of its follow-up bestseller The Black Swan, Fooled by Randomness is a little masterpiece on its own. Nassim Nicholas Taleb will leave you disillusioned about our decision making abilities: we’re ridden with biases, and tend to see patterns in largely random collections of events. More often than we’d like to admit, it’s luck, not skill or hard work, that drives success. Normative sciences get bashed too, with this memorable quote: “normative economics is like religion without the aesthetics”.

5.   Statistics Done Wrong, Alex Reinhart (2015)

A cautionary tale on applied statistics, that’s both shocking and educational. Statistical mistakes are common, and are not necessarily a domain of amateurs. Alex Reinhart discusses the typical errors – some motivated by cold-blooded deception – present in statistical analysis, published literature, and peer-reviewed papers. The book leaves you questioning everything you know, but not frustrated: Statistics Done Wrong is both a research evaluation framework and a guide to creating meaningful analyses.

6.   Small Data: The Tiny Clues That Uncover Huge Trends, Martin Lindstrom (2016)

SMALL DATA combines armchair travel with forensic psychology in an interlocking series of international clue-gathering detective stories. It shows Lindstrom using his proprietary CLUES Framework – where big data is merely one part of the overall puzzle – to get radically close to consumers and come up with the counter-intuitive insights that have in some cases helped transform entire industries.

7.   Show Me the Numbers: Designing Tables and Graphs to Enlighten, Stephen Few (2004)

A must-have for those excited about telling stories with data. Information visualisation is a skill, and this book is a practical selection of great advice, awful ideas to avoid, and some pragmatism helpful in mastering that skill.

8.  Bad Science, Ben Goldacre (2008)

Ben Goldarce’s work is a one-man’s crusade against pseudo-science. He examines evidence behind trends in healthcare: nutritionists, homeopathy, and alternative medicine to name a few. Goldarce cautions of misleading jargon and PR tricks we fell for and vouches for transparency in research. It main focus may be the health industry, but the mindset is universally applicable: as much now as it was 10 years ago. After all, we’ve just learnt that most of the water companies in the UK use the medieval practice of water dowsing despite no scientific evidence for its effectiveness.

9.   Weapons of Math Destruction. How big data increases inequality and threatens democracy, Cathy O’Neil (2016)

A fascinating read on the corporate and government applications of mathematical models. Cathy O’Neil talks black-box algorithms and built-in biases that affect our everyday lives: job, credit, and education-wise. A stimulating must-read for the modern times.

10.   Dataclysm. Who we are (When We Think No One’s Looking), Christian Rudder (2014)

Christian Rudder, a co-founder of OK Cupid, mined the website’s data to find what love is: in numbers. At the same time scary and fun, it’s a data-packed account of our lies, unconscious preferences, and occasional racism in internet dating. To quote Nick Paumgarten from the The New Yorker: “he doesn’t wring or clap his hands over the big-data phenomenon (see N.S.A., Google ads, that sneaky Fitbit) so much as plunge them into big data and attempt to pull strange creatures from the murky depths.”

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