Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics - we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us”.

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first audiobook of its kind, The Kaggle Book assembles in one place the techniques and skills you'll need for success in competitions, data science projects, and beyond.

With the world going remote, and data science following along, you can start and pursue a career in data right here, right now. How do I start? What do I need to know? What is data analytics? I'm sure one, if not all, of these questions have run through your mind. Well, the answer is data literacy: the ability to read, understand, and communicate data into information. Building a solid foundation of practical knowledge is the first step to being successful in data. From there, you can finally decide what direction you should go in and start gaining the required knowledge.

Aubrey Clayton traces the history of how statistics went astray, beginning with the groundbreaking work of the 17th-century mathematician Jacob Bernoulli and winding through gambling, astronomy, and genetics. Clayton recounts the feuds among rival schools of statistics, exploring the surprisingly human problems that gave rise to the discipline and the all-too-human shortcomings that derailed it.

Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger - all by the time he was 30. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data.

Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence - and even more so if we hope to participate in the future, rather than being simple bystanders.

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous real-world examples - ready for immediate application to your next graph or presentation.

From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you'll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.

From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of human memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

"Correlation does not imply causation". This mantra has been invoked by scientists for decades and has led to a virtual prohibition on causal talk. But today, that taboo is dead. The causal revolution, sparked by Judea Pearl and his colleagues, has cut through a century of confusion and placed causality - the study of cause and effect - on a firm scientific basis.

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee.

Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

In their eagerly awaited book, Kim and Spear bring to light a new theory of high-achieving organizations. They examine how companies solve the most important problems better, faster, and easier than their competitors by quickly and regularly closing the gap between aspirations and real-world success. This book teaches companies that are struggling to perform how to achieve the continual greatness seen in the best of the best.

Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia's views on crime and punishment, the psychology of slime molds, what Facebook can and can't figure out about you, and the existence of God.

Since its first publication, Michael J. Mauboussin's popular guide to wise investing has been translated into eight languages and has been named best business book by BusinessWeek and best economics book by Strategy+Business. Now updated to reflect current research and expanded to include new chapters on investment philosophy, psychology, and strategy and science as they pertain to money management.

What is economic growth? And why, historically, has it occurred in only a few places? Previous efforts to answer these questions have focused on institutions, geography, finances, and psychology. But according to MIT's anti-disciplinarian César Hidalgo, understanding the nature of economic growth demands transcending the social sciences and including the natural sciences of information, networks, and complexity. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order.

Many people today are so dazzled by the long-term potential for artificial intelligence that they overlook the much clearer and more immediate potential for a new form of "collective intelligence": the intelligence of groups of people and computers working together. In Superminds, Thomas Malone explains what we need to do to take advantage of this potential. Groundbreaking and utterly fascinating, Superminds will change the way you work - both with others and with computers - for the better.

The Mind of the Market will change the way we think about the economics of everyday life. Drawing on research from neuroeconomics, Michael Shermer explores what brain scans reveal about bargaining, snap purchases, and how trust is established in business. Utilizing experiments in behavioral economics, Shermer shows why people hang on to losing stocks and failing companies, why business negotiations often disintegrate into emotional tit-for-tat disputes, and why money does not make us happy.

The widening gap between rich and poor means dealing with one big, uncomfortable truth: If you're not at the top, you're at the bottom. The global labor market is changing radically thanks to growth at the high end and the low. 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.

Why are cellphone plans so much more expensive in the United States than in Europe? It seems a simple question. But the search for an answer took Thomas Philippon on an unexpected journey through some of the most complex and hotly debated issues in modern economics. Ultimately, he reached a surprising conclusion: American markets, once a model for the world, are giving up on healthy competition.

Picture an early scene from The Wizard of Oz: Dorothy hurries home as a tornado gathers in what was once a clear Kansas sky. Hurriedly, she seeks shelter in the storm cellar under the house, but, finding it locked, takes cover in her bedroom. We all know how that works out for her.Many investors these days are a bit like Dorothy, putting their faith in something as solid and trustworthy as a house (or, say, real estate). But market disruptions - storms - seem to arrive without warning, leaving us little time to react.

In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it.

It has long been recognized that most standard of living increases are associated with advances in technology, not the accumulation of capital. Yet it has also become clear that what truly separates developed from less developed countries is not just a gap in resources or output but a gap in knowledge. In fact the pace at which developing countries grow is largely determined by the pace at which they close that gap. Therefore, how countries learn and become more productive is key to understanding how they grow and develop, especially over the long term.

In The Second Machine Age, Andrew McAfee and Erik Brynjolfsson predicted some of the far-reaching effects of digital technologies on our lives and businesses. Now they’ve written a guide to help listeners make the most of our collective future. Machine | Platform | Crowd outlines the opportunities and challenges inherent in the science fiction technologies that have come to life in recent years, like self-driving cars and 3D printers, online platforms for renting outfits and scheduling workouts, or crowd-sourced medical research and financial instruments.

Many scientific and philosophical ideas are so powerful that they can be applied to our lives at home, work, and school to help us think smarter and more effectively about our behavior and the world around us. Surprisingly, many of these ideas remain unknown to most of us. In Mindware, the world-renowned psychologist Richard Nisbett presents these ideas in clear and accessible detail, offering a tool kit for better thinking and wiser decisions.

This book predicts the decline of today's professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want doctors, teachers, accountants, architects, the clergy, consultants, lawyers, and many others to work as they did in the 20th century.

Many blame today's economic inequality, stagnation, and political instability on the free market. The solution is to rein in the market, right? Radical Markets turns this thinking - and pretty much all conventional thinking about markets, both for and against - on its head. The book reveals bold new ways to organize markets for the good of everyone.

The Great Mental Models: General Thinking Concepts is the first book in The Great Mental Models series designed to upgrade your thinking with the best, most useful and powerful tools so you always have the right one on hand. This volume details nine of the most versatile all-purpose mental models you can use right away to improve your decision making, your productivity, and how clearly you see the world.

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars.

Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently bingeable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics - we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us”.

With the world going remote, and data science following along, you can start and pursue a career in data right here, right now. How do I start? What do I need to know? What is data analytics? I'm sure one, if not all, of these questions have run through your mind. Well, the answer is data literacy: the ability to read, understand, and communicate data into information. Building a solid foundation of practical knowledge is the first step to being successful in data. From there, you can finally decide what direction you should go in and start gaining the required knowledge.

Recent years have seen the introduction of concepts from the new and exciting field of complexity science that have captivated the attention of economists, sociologists, engineers, businesspeople, and many others. These include tipping points, the wisdom of crowds, six degrees of separation (or Kevin Bacon), and emergence. Complexity science can shed light on why businesses or economies succeed and fail, how epidemics spread and can be stopped, and what causes ecological systems to rebalance themselves after a disaster.

Aubrey Clayton traces the history of how statistics went astray, beginning with the groundbreaking work of the 17th-century mathematician Jacob Bernoulli and winding through gambling, astronomy, and genetics. Clayton recounts the feuds among rival schools of statistics, exploring the surprisingly human problems that gave rise to the discipline and the all-too-human shortcomings that derailed it.

Why is the Mona Lisa the most famous painting in the world? Why did Facebook succeed when other social networking sites failed? Did the surge in Iraq really lead to less violence? How much can CEO’s impact the performance of their companies? And does higher pay incentivize people to work hard? If you think the answers to these questions are a matter of common sense, think again.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence - and even more so if we hope to participate in the future, rather than being simple bystanders.

This new edition - more than 50 percent new and revised - is a significant update from the previous one and reveals how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics, such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.

This friendly guide teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. This accesible introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.