With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.Work hands-on with three practical data analysis projects based on casino gamesStore, retrieve, and change data values in your computer's memoryWrite programs and simulations that outperform those written by typical R usersUse R programming tools such as if else statements, for loops, and S3 classesLearn how to write lightning-fast vectorized R codeTake advantage of R's package system and debugging toolsPractice and apply R programming concepts as you learn them

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

    One of the world's most eminent thinkers, Searle dismantles these theories as he presents a vividly written, comprehensive introduction to the mind. He begins with a look at the twelve problems of philosophy of mind--which he calls Descartes and Other Disasters--problems which he returns to throughout the volume, as he illuminates such topics as materialism, consciousness, the mind-body problem, intentionality, mental causation, free will, and the self. The book offers a refreshingly direct and engaging introduction to one of the most intriguing areas of philosophy.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: A flexible step-by-step journey that equips you to achieve great data visualization.A curated collection of classic and contemporary examples, giving illustrations of good and bad practice Examples on every page to give creative inspiration Illustrations of good and bad practice show you how to critically evaluate and improve your own work Advice and experience from the best designers in the field Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available

    Clear, comprehensive coverage of utility theory, 2-person zero-sum games, 2-person non-zero-sum games, n-person games, individual and group decision-making, more. Bibliography.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    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.

    Casti contemplates an imaginary evening of intellectual inquiry—a sort of “My Dinner with” not Andre, but five of the most brilliant thinkers of the twentieth century.Imagine, if you will, one stormy summer evening in 1949, as novelist and scientist C. P. Snow, Britain’s distinguished wartime science advisor and author of The Two Cultures, invites four singular guests to a sumptuous seven-course dinner at his alma mater, Christ’s College, Cambridge, to discuss one of the emerging scientific issues of the day: Can we build a machine that could duplicate human cognitive processes? The distinguished guest list for Snow’s dinner consists of physicist Erwin Schrodinger, inventor of wave mechanics; Ludwig Wittgenstein, the famous twentieth-century philosopher of language, who posited two completely contradictory theories of human thought in his lifetime; population geneticist/science popularizer J.B.S. Haldane; and Alan Turing, the mathematician/codebreaker who formulated the computing scheme that foreshadowed the logical structure of all modern computers. Capturing not only their unique personalities but also their particular stands on this fascinating issue, Casti dramatically shows what each of these great men might have argued about artificial intelligence, had they actually gathered for dinner that midsummer evening.With Snow acting as referee, a lively intellectual debate unfolds. Philosopher Wittgenstein argues that in order to become conscious, a machine would have to have life experiences similar to those of human beings—such as pain, joy, grief, or pleasure. Biologist Haldane offers the idea that mind is a separate entity from matter, so that regardless of how sophisticated the machine, only flesh can bond with that mysterious force called intelligence. Both physicist Schrodinger and, of course, computer pioneer Turing maintain that it is not the substance, but rather the organization of that substance, that makes a mind conscious.With great verve and skill, Casti recreates a unique and thrilling moment of time in the grand history of scientific ideas. Even readers who have already formed an opinion on artificial intelligence will be forced to reopen their minds on the subject upon reading this absorbing narrative. After almost four decades, the solutions to the epic scientific and philosophical problems posed over this meal in C. P. Snow’s old rooms at Christ’s College remains tantalizingly just out of reach, making this adventure into scientific speculation as valid today as it was in 1949.

    In addition to changing how we live, these tools enable us to collect and process data about human behavior on a scale never before imaginable, offering entirely new approaches to core questions about social behavior. Bit by Bit is the key to unlocking these powerful methods--a landmark book that will fundamentally change how the next generation of social scientists and data scientists explores the world around us.Bit by Bit is the essential guide to mastering the key principles of doing social research in this fast-evolving digital age. In this comprehensive yet accessible book, Matthew Salganik explains how the digital revolution is transforming how social scientists observe behavior, ask questions, run experiments, and engage in mass collaborations. He provides a wealth of real-world examples throughout and also lays out a principles-based approach to handling ethical challenges.Bit by Bit is an invaluable resource for social scientists who want to harness the research potential of big data and a must-read for data scientists interested in applying the lessons of social science to tomorrow's technologies.Illustrates important ideas with examples of outstanding researchCombines ideas from social science and data science in an accessible style and without jargonGoes beyond the analysis of "found" data to discuss the collection of "designed" data such as surveys, experiments, and mass collaborationFeatures an entire chapter on ethicsIncludes extensive suggestions for further reading and activities for the classroom or self-study

    In this richly illustrated book, accessible examples are used to show how information theory can be understood in terms of everyday games like '20 Questions', and the simple MatLab programs provided give hands-on experience of information theory in action. Written in a tutorial style, with a comprehensive glossary, this text represents an ideal primer for novices who wish to become familiar with the basic principles of information theory.Download chapter 1 from http://jim-stone.staff.shef.ac.uk/Boo...

    This book includes the following features:- Selective vocabulary found on recent SATs and PSATs used in context. No more memorizing the definitions of long lists of seemingly random words in a vacuum.- Relevant, vivid, and memorable examples from pop culture, historic events, literature, and contemporary issues.- Six easy-to-tackle chapters- A Fast Review for each chapter, with quick definitions- A Final Review with sentence completion questions just like real SATs and PSATs, including complete solution explanationsBuilding on the success of previous editions, the authors of "Direct Hits Core Vocabulary of the SAT" consulted secondary school teachers, tutors, parents, and students from around the world to ensure that these words and illustrations are on target to prepare you for success on the SAT. You will find that the process is effective, worthwhile, and even fun!

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    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

    This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.