Here are 99 books that Information is Beautiful fans have personally recommended if you like
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I’m the Science Director of the Science Museum Group, based at the Science Museum in London, and visiting professor at the Dunn School, University of Oxford, and Department of Chemistry, University College London. Every time I write a book I swear that it will be my last and yet I'm now working on my ninth, after earlier forays into the physics of Christmas and the love life of Albert Einstein. Working with Peter Coveney of UCL, we're exploring ideas about computation and complexity we tackled in our two earlier books, along with the revolutionary implications of creating digital twins of people from the colossal amount of patient data now flowing from labs worldwide.
Over a single year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, exchanged hand-drawn postcards to chart the granular details of their lives using clusters, plots, and graphs. We featured the outpourings of these talented “information designers” in a 2016 Science Museum exhibition on big data and these striking images, in turn, paved the way for their book, Dear Data, which provides a remarkable portrait of these artists. An intimate and human take on big datathat invites us all to ponder how to represent our own lives.
From an award-winning project comes an inspiring, collaborative book that makes data artistic, personal - and open to all
Each week for a year, Giorgia and Stefanie sent each other a postcard describing what had happened to them during that week around a particular theme. But they didn't write it, they drew it: a week of smiling, a week of apologies, a week of desires.
Presenting their fifty-two cards, along with thoughts and ideas about the data-drawing process, Dear Data hopes to inspire you to draw, slow down and make connections with other people, to see the world through a…
I’m the Science Director of the Science Museum Group, based at the Science Museum in London, and visiting professor at the Dunn School, University of Oxford, and Department of Chemistry, University College London. Every time I write a book I swear that it will be my last and yet I'm now working on my ninth, after earlier forays into the physics of Christmas and the love life of Albert Einstein. Working with Peter Coveney of UCL, we're exploring ideas about computation and complexity we tackled in our two earlier books, along with the revolutionary implications of creating digital twins of people from the colossal amount of patient data now flowing from labs worldwide.
This might not look like a big data book but, for me, the race to read the human genome marks the birth of big data in biology, in the form of a tsunami of DNA sequencing data. I edited Craig Venter’s A Life Decoded, the first genetic autobiography, which explored the implications of becoming the first person to gaze upon all six billion ‘letters’ of their own genetic code. While working on Craig’s extraordinary story I came across The Genome War and thought James Shreeve did a brilliant job in describing the drama, rivalry, and personalities in the race to sequence the very first human genomes between government-backed scientists and Celera, Craig’s company.
The long-awaited story of the science, the business, the politics, the intrigue behind the scenes of the most ferocious competition in the history of modern science—the race to map the human genome. On May 10, 1998, biologist Craig Venter, director of the Institute for Genomic Research, announced that he was forming a private company that within three years would unravel the complete genetic code of human life—seven years before the projected finish of the U.S. government’s Human Genome Project. Venter hoped that by decoding the genome ahead of schedule, he would speed up the pace of biomedical research and save…
I’m a mathematics professor who ended up writing the internationally bestselling novel The Death of Vishnu (along with two follow-ups) and became better known as an author. For the past decade and a half, I’ve been using my storytelling skills to make mathematics more accessible (and enjoyable!) to a broad audience. Being a novelist has helped me look at mathematics in a new light, and realize the subject is not so much about the calculations feared by so many, but rather, about ideas. We can all enjoy such ideas, and thereby learn to understand, appreciate, and even love math.
A primary reason to love math is because of its usefulness. This book shows two sides of math’s applicability, since it is so ubiquitously used in various algorithms.
On the one hand, such usage can be good, because statistical inferences can make our life easier and enrich it. On the other, when these are not properly designed or monitored, it can lead to catastrophic consequences. Mathematics is a powerful force, as powerful as wind or fire, and needs to be harnessed just as carefully.
Cathy O’Neil’s message in this book spoke deeply to me, reminding me that I need to be always vigilant about the subject I love not being deployed carelessly.
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…
I’m the Science Director of the Science Museum Group, based at the Science Museum in London, and visiting professor at the Dunn School, University of Oxford, and Department of Chemistry, University College London. Every time I write a book I swear that it will be my last and yet I'm now working on my ninth, after earlier forays into the physics of Christmas and the love life of Albert Einstein. Working with Peter Coveney of UCL, we're exploring ideas about computation and complexity we tackled in our two earlier books, along with the revolutionary implications of creating digital twins of people from the colossal amount of patient data now flowing from labs worldwide.
‘They trust me….dumb f*cks.’ This telling exchange from the Harvard days of Facebook co-founder and CEO, Mark Zuckerberg appears in An Ugly Truth, which shines a harsh light on the tech behemoth that, ultimately, is built on the data of billions of people. As Meta, Zuckerberg’s new business incarnation, wafts into the virtual worlds of the metaverse, the story of Facebook is far from over, which makes this engaging book a tad unsatisfying. Nonetheless, it is a vivid example of how with Big Data comes Big Responsibility.
'[A] careful, comprehensive interrogation of every major Facebook scandal. An Ugly Truth provides the kind of satisfaction you might get if you hired a private investigator to track a cheating spouse: it confirms your worst suspicions and then gives you all the dates and details you need to cut through the company's spin' New York Times __________________________________________ Award-winning New York Times reporters Sheera Frenkel and Cecilia Kang unveil the tech story of our times in this riveting, behind-the-scenes expose that offers the definitive account of Facebook's fall from grace. Once one of Silicon Valley's…
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Every enterprise application creates data, whether it's log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.
Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you'll learn Kafka's…
Accurate and precise forecasting is essential for successful planning and policy from economics to epidemiology. We have been keen to understand why so many forecasts turn out to be highly inaccurate since making dreadful forecasts ourselves, and advising UK government agencies (Treasury, Parliament, Bank of England) during turbulent periods. As simple extrapolation often beats model-based forecasting, we have been developing improved methods that draw on the best aspects of both, and have published more than 60 articles and 6 books attracting more than 6000 citations by other scholars. Our recommended books cover a wide range of forecasting methods—suggesting there is no optimal way to look into the future.
When can we trust a forecast? Given how often forecasts end up being very wide of the mark, a degree of scepticism might well be warranted. Paul Goodwin provides an entertaining account of forecasting, arguing that intuition may serve us well in some settings, but that computer-based analysis of big data might be expected to prevail in others.
Whether it's an unforeseen financial crash, a shock election result or a washout summer that threatens to ruin a holiday in the sun, forecasts are part and parcel of our everyday lives. We rely wholeheartedly on them, and become outraged when things don't go exactly to plan.
But should we really put so much trust in predictions? Perhaps gut instincts can trump years of methodically compiled expert knowledge? And when exactly is a forecast not a forecast? Forewarned will answer all of these intriguing questions, and many more.
Packed with fun anecdotes and startling facts, Forewarned is a myth-busting guide…
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Apache Spark has a very high point of entry for newcomers to the Big Data ecosystem.
However, it is a key tool that almost everyone is using for running distributed processing. I recommend everyone to read this book before delving into production solutions based on Apache Spark.
This book will allow you to alleviate many spark problems, such as serialization, memory utilization, and parallelization of processing.
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for…
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Designing Data-Intensive Applications is the best book if you want to learn about the main principles behind every system that is able to store and process big amounts of data.
You'll learn about distributed storage systems, their tradeoffs (availability, consistency, fault-tolerance), streaming processing systems, and main algorithms.
Those are the critical concepts behind almost every successful company that needs to create scalable solutions.
Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain…
I grew up and completed the formative years of my college education in Cape Town, South Africa, while active also in anti-apartheid struggles. My Ph.D. dissertation in the 1980s focused on the elaboration of key racial ideas in the modern history of philosophy. I have published extensively on race and racism in the U.S. and globally, in books, articles, and public media. My interests have especially focused on the transforming logics and expressions of racism over time, and its updating to discipline and constrain its conventional targets anew and new targets more or less conventionally. My interest has always been to understand racism in order to face it down.
Digital technology, like technology generally, is commonly assumed to be value neutral. Wendy Chun reveals that structurally embedded in digital operating systems and data collection are values that reproduce and extend existing modes of discriminating while also originating new ones. In prompting and promoting the grouping together of people who are alike—in habits, culture, looks, and preferences—the logic of the algorithm reproduces and amplifies discriminatory trends. Chun reveals how the logics of the digital reinforce the restructuring of racism by the neoliberal turn that my own book lays out.
How big data and machine learning encode discrimination and create agitated clusters of comforting rage.
In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt…
I’ve worked with business leaders on pay projects all over the world, at companies like Nike and Starbucks, in places like Brazil, Mexico, Vietnam, Singapore, the UAE, and all over Europe. While many business books are written from a theoretical or academic perspective, I bring an operator’s perspective. I get to work out the ideas in my book, Fair Pay, on a daily basis, and so I wrote the book to be a realistic and practical guide for understanding the perspectives of business leaders, human resources, and the typical employee.
Changing careers from publishing to tech is a path not often traveled. Wiener made this jump from a world legendary for its light pay compensated by romanticism, to an industry best known for generous “perks that landed somewhere between the collegiate and the feudal.” Wiener’s experience makes for one of the most entertaining books I’ve read in years—she is a gifted writer and unafraid to call out the over-seriousness of the tech bro mentality as an ultimately “dreary” worldview.
A NEW YORK TIMES BESTSELLER. ONE OF THE NEW YORK TIMES'S 10 BEST BOOKS OF 2020.
Named one of the Best Books of 2020 by The Washington Post, The Atlantic, NPR, the Los Angeles Times, ELLE, Esquire, Parade, Teen Vogue, The Boston Globe, Forbes, The Times (UK), Fortune, Chicago Tribune, Glamour, The A.V. Club, Vox, Jezebel, Town & Country, OneZero, Apartment Therapy, Good Housekeeping, PopMatters, Electric Literature, Self, The Week (UK) and BookPage.A New York Times Book Review Editors' Choice and a January 2020 IndieNext Pick.
"A definitive document of a world in transition: I won't be alone in returning…
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