In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art.

When "metadata" became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was "only" collecting metadata about phone calls - information about the caller, the recipient, the time, the duration, the location - and not recordings of the conversations themselves? Or does phone call metadata reveal more than it seems?

A few decades into the digital era, scientists discovered that thinking in terms of computation made possible an entirely new way of organizing scientific investigation; eventually, every field had a computational branch: computational physics, computational biology, computational sociology. More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview.

The history of computing could be told as the story of hardware and software or the story of the Internet or the story of "smart" handheld devices, with subplots involving IBM, Microsoft, Apple, Facebook, and Twitter. In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development.

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.

Amazon.com started off delivering books through the mail. But its visionary founder, Jeff Bezos, wasn't content with being a bookseller. He wanted Amazon to become the everything store, offering limitless selection and seductive convenience at disruptively low prices. To do so, he developed a corporate culture of relentless ambition and secrecy that's never been cracked. Until now.

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.

Expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data-centric issues. Discover two comprehensive manuscripts in one audiobook.

Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja.

"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.

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.

Chemistry and Our Universe: How It All Works is your in-depth introduction to this vital field, taught through 60 engaging half-hour lectures that are suitable for any background or none at all. Covering a year’s worth of introductory general chemistry at the college level, plus intriguing topics that are rarely discussed in the classroom, this amazingly comprehensive course requires nothing more advanced than high-school math. Your guide is Professor Ron B. Davis, Jr., a research chemist and award-winning teacher at Georgetown University.

We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.

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.

In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art.

Critical thinking is regularly cited as an essential 21st century skill, the key to success in school and work. Given our propensity to believe fake news, draw incorrect conclusions, and make decisions based on emotion rather than reason, it might even be said that critical thinking is vital to the survival of a democratic society. But what, exactly, is critical thinking? Haber describes the term's origins in such disciplines as philosophy, psychology, and science.

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.

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.

The history of computing could be told as the story of hardware and software or the story of the Internet or the story of "smart" handheld devices, with subplots involving IBM, Microsoft, Apple, Facebook, and Twitter. In this concise and accessible account of the invention and development of digital technology, computer historian Paul Ceruzzi offers a broader and more useful perspective. He identifies four major threads that run throughout all of computing's technological development.