But where do they come from? What kind of environment breeds them? What sparks the flash of brilliance? How do we generate the breakthrough technologies that push forward our lives, our society, our culture? Steven Johnson's answers are revelatory as he identifies the seven key patterns behind genuine innovation, and traces them across time and disciplines. From Darwin and Freud to the halls of Google and Apple, Johnson investigates the innovation hubs throughout modern time and pulls out the approaches and commonalities that seem to appear at moments of originality.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Ideas for new businesses, solutions to the world's problems, and artistic breakthroughs are common, but great execution is rare. According to Scott Belsky, the capacity to make ideas happen can be developed by anyone willing to develop their organizational habits and leadership capability. That's why he founded Behance, a company that helps creative people and teams across industries develop these skills. Belsky has spent six years studying the habits of creative people and teams that are especially productive-the ones who make their ideas happen time and time again. After interviewing hundreds of successful creatives, he has compiled their most powerful-and often counterintuitive-practices, such as: •Generate ideas in moderation and kill ideas liberally •Prioritize through nagging •Encourage fighting within your team While many of us obsess about discovering great new ideas, Belsky shows why it's better to develop the capacity to make ideas happen-a capacity that endures over time.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of "data-driven apps." Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by "data science." A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products.

    A simple web search picks out a handful of relevant needles from the world's biggest haystack: the billions of pages on the World Wide Web. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers; and we use digital signatures to verify the identity of the websites we visit. How do our computers perform these tasks with such ease? This is the first book to answer that question in language anyone can understand, revealing the extraordinary ideas that power our PCs, laptops, and smartphones. Using vivid examples, John MacCormick explains the fundamental "tricks" behind nine types of computer algorithms, including artificial intelligence (where we learn about the "nearest neighbor trick" and "twenty questions trick"), Google's famous PageRank algorithm (which uses the "random surfer trick"), data compression, error correction, and much more. These revolutionary algorithms have changed our world: this book unlocks their secrets, and lays bare the incredible ideas that our computers use every day.

    And, the Director of Google Ideas, Jared Cohen, formerly an advisor to both Secretaries of State Condoleezza Rice and Hillary Clinton.Never before has the future been so vividly and transparently imagined. From technologies that will change lives (information systems that greatly increase productivity, safety and our quality of life, thought controlled motion technology that can revolutionize medical procedures, and near-perfect translation technology that allows us to have more diversified interactions) to our most important future considerations (curating our online identity and fighting those who would do harm with it) to the widespread political change that will transform the globe (through transformations in conflict, increasingly active and global citizenries, a new wave of cyber-terrorism and states operating simultaneously in the physical and virtual realms) to the ever present threats to our privacy and security, Schmidt and Cohen outline in great detail and scope all the promise and peril awaiting us in the coming decades.

    Forget short-term symptoms like Donald Trump and Brexit, it is innovation itself that explains them and that will itself shape the 21st century for good and ill. Yet innovation remains a mysterious process, poorly understood by policy makers and businessmen, hard to summon into existence to order, yet inevitable and inexorable when it does happen.Matt Ridley argues in this book that we need to change the way we think about innovation, to see it as an incremental, bottom-up, fortuitous process that happens to society as a direct result of the human habit of exchange, rather than an orderly, top-down process developing according to a plan. Innovation is crucially different from invention, because it is the turning of inventions into things of practical and affordable use to people. It speeds up in some sectors and slows down in others. It is always a collective, collaborative phenomenon, not a matter of lonely genius. It is gradual, serendipitous, recombinant, inexorable, contagious, experimental and unpredictable. It happens mainly in just a few parts of the world at any one time. It still cannot be modelled properly by economists, but it can easily be discouraged by politicians. Far from there being too much innovation, we may be on the brink of an innovation famine.Ridley derives these and other lessons, not with abstract argument, but from telling the lively stories of scores of innovations, how they started and why they succeeded or in some cases failed. He goes back millions of years and leaps forward into the near future. Some of the innovation stories he tells are about steam engines, jet engines, search engines, airships, coffee, potatoes, vaping, vaccines, cuisine, antibiotics, mosquito nets, turbines, propellers, fertiliser, zero, computers, dogs, farming, fire, genetic engineering, gene editing, container shipping, railways, cars, safety rules, wheeled suitcases, mobile phones, corrugated iron, powered flight, chlorinated water, toilets, vacuum cleaners, shale gas, the telegraph, radio, social media, block chain, the sharing economy, artificial intelligence, fake bomb detectors, phantom games consoles, fraudulent blood tests, faddish diets, hyperloop tubes, herbicides, copyright and even – a biological innovation -- life itself.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company, from analysts and management to the C-Suite and the board.Through interviews and examples from data scientists and analytics leaders in a variety of industries, author Carl Anderson explains the analytics value chain you need to adopt when building predictive business models—from data collection and analysis to the insights and leadership that drive concrete actions. You’ll learn what works and what doesn’t, and why creating a data-driven culture throughout your organization is essential. Start from the bottom up: learn how to collect the right data the right way Hire analysts with the right skills, and organize them into teams Examine statistical and visualization tools, and fact-based story-telling methods Collect and analyze data while respecting privacy and ethics Understand how analysts and their managers can help spur a data-driven culture Learn the importance of data leadership and C-level positions such as chief data officer and chief analytics officer

    Then came SuperFreakonomics, a documentary film, an award-winning podcast, and more.Now, with Think Like a Freak, Steven D. Levitt and Stephen J. Dubner have written their most revolutionary book yet. With their trademark blend of captivating storytelling and unconventional analysis, they take us inside their thought process and teach us all to think a bit more productively, more creatively, more rationally—to think, that is, like a Freak.Levitt and Dubner offer a blueprint for an entirely new way to solve problems, whether your interest lies in minor lifehacks or major global reforms. As always, no topic is off-limits. They range from business to philanthropy to sports to politics, all with the goal of retraining your brain. Along the way, you’ll learn the secrets of a Japanese hot-dog-eating champion, the reason an Australian doctor swallowed a batch of dangerous bacteria, and why Nigerian e-mail scammers make a point of saying they’re from Nigeria.Some of the steps toward thinking like a Freak:First, put away your moral compass—because it’s hard to see a problem clearly if you’ve already decided what to do about it.Learn to say “I don’t know”—for until you can admit what you don’t yet know, it’s virtually impossible to learn what you need to.Think like a child—because you’ll come up with better ideas and ask better questions.Take a master class in incentives—because for better or worse, incentives rule our world.Learn to persuade people who don’t want to be persuaded—because being right is rarely enough to carry the day.Learn to appreciate the upside of quitting—because you can’t solve tomorrow’s problem if you aren’t willing to abandon today’s dud.Levitt and Dubner plainly see the world like no one else. Now you can too. Never before have such iconoclastic thinkers been so revealing—and so much fun to read.

    In Dataclysm, Christian Rudder uses it to show us who we truly are.   For centuries, we’ve relied on polling or small-scale lab experiments to study human behavior. Today, a new approach is possible. As we live more of our lives online, researchers can finally observe us directly, in vast numbers, and without filters. Data scientists have become the new demographers.   In this daring and original book, Rudder explains how Facebook "likes" can predict, with surprising accuracy, a person’s sexual orientation and even intelligence; how attractive women receive exponentially more interview requests; and why you must have haters to be hot. He charts the rise and fall of America’s most reviled word through Google Search and examines the new dynamics of collaborative rage on Twitter. He shows how people express themselves, both privately and publicly. What is the least Asian thing you can say? Do people bathe more in Vermont or New Jersey? What do black women think about Simon & Garfunkel? (Hint: they don’t think about Simon & Garfunkel.) Rudder also traces human migration over time, showing how groups of people move from certain small towns to the same big cities across the globe. And he grapples with the challenge of maintaining privacy in a world where these explorations are possible.   Visually arresting and full of wit and insight, Dataclysm is a new way of seeing ourselves—a brilliant alchemy, in which math is made human and numbers become the narrative of our time.

    The blades start moving in 60 seconds. What do you do? If you want to work at Google, or any of America's best companies, you need to have an answer to this and other puzzling questions. Are You Smart Enough to Work at Google? guides readers through the surprising solutions to dozens of the most challenging interview questions. The book covers the importance of creative thinking, ways to get a leg up on the competition, what your Facebook page says about you, and much more. Are You Smart Enough to Work at Google? is a must-read for anyone who wants to succeed in today's job market.

    About three quarters of the jobs created in the United States since the great recession pay only a bit more than minimum wage. Still, the United States has more millionaires and billionaires than any country ever, and we continue to mint them.In this eye-opening book, renowned economist and bestselling author Tyler Cowen explains that phenomenon: High earners are taking ever more advantage of machine intelligence in data analysis and achieving ever-better results. Meanwhile, low earners who haven’t committed to learning, to making the most of new technologies, have poor prospects. Nearly every business sector relies less and less on manual labor, and this fact is forever changing the world of work and wages. A steady, secure life somewhere in the middle—average—is over.With The Great Stagnation, Cowen explained why median wages stagnated over the last four decades; in Average Is Over he reveals the essential nature of the new economy, identifies the best path forward for workers and entrepreneurs, and provides readers with actionable advice to make the most of the new economic landscape. It is a challenging and sober must-read but ultimately exciting, good news. In debates about our nation’s economic future, it will be impossible to ignore.

    Friedman shows that we have entered an age of dizzying acceleration--and explains how to live in it. Due to an exponential increase in computing power, climbers atop Mount Everest enjoy excellent cell-phone service and self-driving cars are taking to the roads. A parallel explosion of economic interdependency has created new riches as well as spiraling debt burdens. Meanwhile, Mother Nature is also seeing dramatic changes as carbon levels rise and species go extinct, with compounding results.How do these changes interact, and how can we cope with them? To get a better purchase on the present, Friedman returns to his Minnesota childhood and sketches a world where politics worked and joining the middle class was an achievable goal. Today, by contrast, it is easier than ever to be a maker (try 3-D printing) or a breaker (the Islamic State excels at using Twitter), but harder than ever to be a leader or merely "average." Friedman concludes that nations and individuals must learn to be fast (innovative and quick to adapt), fair (prepared to help the casualties of change), and slow (adept at shutting out the noise and accessing their deepest values). With vision, authority, and wit, Thank You for Being Late establishes a blueprint for how to think about our times.