Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    A classic text by the author who developed ELIZA, a natural-language processing system.

    The topic everyone in the world should be talking about.

    It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking.Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things.If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.

    For centuries, such fears have been misplaced, and many economists maintain that they remain so today. But as Daniel Susskind demonstrates, this time really is different. Breakthroughs in artificial intelligence mean that all kinds of jobs are increasingly at risk.Drawing on almost a decade of research in the field, Susskind argues that machines no longer need to think like us in order to outperform us, as was once widely believed. As a result, more and more tasks that used to be far beyond the capability of computers – from diagnosing illnesses to drafting legal contracts, from writing news reports to composing music – are coming within their reach. The threat of technological unemployment is now real.This is not necessarily a bad thing, Susskind emphasizes. Technological progress could bring about unprecedented prosperity, solving one of humanity’s oldest problems: how to make sure that everyone has enough to live on. The challenges will be to distribute this prosperity fairly, to constrain the burgeoning power of Big Tech, and to provide meaning in a world where work is no longer the center of our lives. Perceptive, pragmatic, and ultimately hopeful, A World Without Work shows the way.

    It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

    Train an em to do some job and copy it a million times: an army of workers is at your disposal. When they can be made cheaply, within perhaps a century, ems will displace humans in most jobs. In this new economic era, the world economy may double in size every few weeks. Some say we can't know the future, especially following such a disruptive new technology, but Professor Robin Hanson sets out to prove them wrong. Applying decades of expertise in physics, computer science, and economics, he uses standard theories to paint a detailed picture of a world dominated by ems. While human lives don't change greatly in the em era, em lives are as different from ours as our lives are from those of our farmer and forager ancestors. Ems make us question common assumptions of moral progress, because they reject many of the values we hold dear. Read about em mind speeds, body sizes, job training and career paths, energy use and cooling infrastructure, virtual reality, aging and retirement, death and immortality, security, wealth inequality, religion, teleportation, identity, cities, politics, law, war, status, friendship and love. This book shows you just how strange your descendants may be, though ems are no stranger than we would appear to our ancestors. To most ems, it seems good to be an em.

    Negroponte's fans will want to get a copy of Being Digital, which is an edited version of the 18 articles he wrote for Wired about "being digital." Negroponte's text is mostly a history of media technology rather than a set of predictions for future technologies. In the beginning, he describes the evolution of CD-ROMs, multimedia, hypermedia, HDTV (high-definition television), and more. The section on interfaces is informative, offering an up-to-date history on visual interfaces, graphics, virtual reality (VR), holograms, teleconferencing hardware, the mouse and touch-sensitive interfaces, and speech recognition. In the last chapter and the epilogue, Negroponte offers visionary insight on what "being digital" means for our future. Negroponte praises computers for their educational value but recognizes certain dangers of technological advances, such as increased software and data piracy and huge shifts in our job market that will require workers to transfer their skills to the digital medium. Overall, Being Digital provides an informative history of the rise of technology and some interesting predictions for its future.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    The coming Augmented Age, however, promises a level of disruption, behavioral shifts and changes that are unparalleled. While consumers today are camping outside of an Apple store waiting to be one of the first to score a new Apple Watch or iPhone, the next generation of wearables will be able to predict if we re likely to have a heart attack and recommend a course of action. We watch news of Google s self-driving cars, but don t likely realize this means progressive cities will have to ban human drives in the next decade because us humans are too risky. Following on from the Industrial or Machine Age, the Space Age and the Digital Age, the Augmented Age will be based on four key disruptive themes Artificial Intelligence, Experience Design, Smart Infrastructure, and Health Tech. Historically the previous ages brought significant disruption and changes, but on a net basis jobs were created, wealth was enhanced, and the health and security of society improved. What will the Augmented Age bring? Will robots take our jobs, and AI s subsume us as inferior intelligences, or will this usher in a new age of abundance? Augmented is a book on future history, but more than that, it is a story about how you will live your life in a world that will change more in the next 20 years than it has in the last 250 years. Are you ready to adapt? Because if history proves anything, you don't have much of a choice."

    SAFe addresses this primary goal, and offers these further benefits: reduced time-to-market, improved quality, stronger alignment across geographically distributed multi-disciplinary teams, and collaboration across teams to deliver meaningful value to customers with reduced cycle time." --Sundaresan Jagadeesan, SW CoE Program Director, Philips To succeed in today's adapt-or-die marketplace, businesses must be able to rapidly change the way they create and deliver value to their customers. Hundreds of the world's most successful companies-including Intel, Capital One, AstraZeneca, Cisco, and Philips-have turned to the Scaled Agile Framework(R) (SAFe(R)) to achieve agility at scale and maintain a competitive edge. SAFe(R) 4.5 Distilled: Applying the Scaled Agile Framework(R) for Lean Enterprises explains how adopting SAFe can quickly improve time to market and increase productivity, quality, and employee engagement. In this book, you will Understand the business case for SAFe: its benefits, the problems it solves, and how to apply it Get an overview of SAFe across all parts of the business: team, program, value stream, and portfolio Learn why SAFe works: the power of SAFe's Lean-Agile mindset, values, and principles Discover how systems thinking, Agile development, and Lean product development form the underlying basis for SAFe Learn how to become a Lean-Agile leader and effectively drive an enterprise-wide transformation Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    Frighteningly often, the influence of technology in and on our lives goes completely unchallenged by citizens and governments. We comfort ourselves with the soothing refrain that technology has no morals and can display no prejudice, and it's only the users of technology who distort certain aspects of it.But is this statement actually true? Dr Robert Smith thinks it is dangerously untrue in the modern era.Having worked in the field of artificial intelligence for over 30 years, Smith reveals the mounting evidence that the mechanical actors in our lives do indeed have, or at least express, morals: they're just not the morals of the progressive modern society that we imagined we were moving towards. Instead, as we are just beginning to see – in the US elections and Brexit to name but a few – there are increasing incidences of machine bigotry, greed and the crass manipulation of our basest instincts.It is easy to assume that these are the result of programmer prejudices or the product of dark forces manipulating the masses through the network of the Internet. But what if there is something more fundamental and explicitly mechanical at play, something inherent within technology itself?This book demonstrates how non-scientific ideas have been encoded deep into our technological infrastructure. Offering a rigorous, fresh perspective on how technology has brought us to this place, Rage Inside the Machine challenges the long-held assumption that technology is an apolitical and amoral force. Shedding light on little-known historical stories and investigating the complex connections between scientific philosophy, institutional prejudice and new technology, this book offers a new, honest and more truly scientific vision of ourselves.