The successful core research text is known for its truly balanced coverage of qualitative and quantitative methods and the author's high-quality writing has made this book a favourite amongst instructors and students.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    Gigabytes, exabytes (that’s one quintillion bytes) of data are sitting on servers across the world. So how can we start to access this explosion of information, this “big data,” and what can it tell us?   Erez Aiden and Jean-Baptiste Michel are two young scientists at Harvard who started to ask those questions. They teamed up with Google to create the Ngram Viewer, a Web-based tool that can chart words throughout the massive Google Books archive, sifting through billions of words to find fascinating cultural trends. On the day that the Ngram Viewer debuted in 2010, more than one million queries were run through it.   On the front lines of Big Data, Aiden and Michel realized that this big dataset—the Google Books archive that contains remarkable information on the human experience—had huge implications for looking at our shared human history. The tool they developed to delve into the data has enabled researchers to track how our language has evolved over time, how art has been censored, how fame can grow and fade, how nations trend toward war. How we remember and how we forget. And ultimately, how Big Data is changing the game for the sciences, humanities, politics, business, and our culture.

    Rather than relying on abstract generalizations and theories, van Manen offers an alternative that taps the unique nature of each human situation.The book offers detailed methodological explications and practical examples of hermeneutic-phenomenological inquiry. It shows how to orient oneself to human experience in education and how to construct a textual question which evokes a fundamental sense of wonder, and it provides a broad and systematic set of approaches for gaining experiential material that forms the basis for textual reflections.Van Manen also discusses the part played by language in educational research, and the importance of pursuing human science research critically as a semiotic writing practice. He focuses on the methodological function of anecdotal narrative in human science research, and offers methods for structuring the research text in relation to the particular kinds of questions being studied. Finally, van Manen argues that the choice of research method is itself a pedagogic commitment and that it shows how one stands in life as an educator.

    In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak.In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science.An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications

    Written specifically for students with no previous experience of research and research methodology, the writing style is simple and clear and the author presents this complex subject in a straightforward way that empowers readers to tackle research with confidence. The book has been revised and updated to include extended coverage of qualitative research methods in addition to existing comprehensive coverage of quantitative methods. There are also brand new learning features such as reflective questions throughout the text to help students consolidate their knowledge.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    Verification that algorithms work is emphasized more than their complexity. An effective use of examples, and huge number of interesting exercises, demonstrate the topics of trees and distance, matchings and factors, connectivity and paths, graph coloring, edges and cycles, and planar graphs. For those who need to learn to make coherent arguments in the fields of mathematics and computer science.

    is the most fun you'll ever have with graphs." -- Bustle Military intelligence analyst and Harvard Law student Tyler Vigen illustrates the golden rule that "correlation does not equal causation" through hilarious graphs inspired by his viral website.Is there a correlation between Nic Cage films and swimming pool accidents? What about beef consumption and people getting struck by lightning? Absolutely not. But that hasn't stopped millions of people from going to tylervigen.com and asking, "Wait, what?" Vigen has designed software that scours enormous data sets to find unlikely statistical correlations. He began pulling the funniest ones for his website and has since gained millions of views, hundreds of thousands of likes, and tons of media coverage. Subversive and clever, Spurious Correlations is geek humor at its finest, nailing our obsession with data and conspiracy theory.

     Is it possible to: - Influence reality with the power of thought? - Push into the universal mind, vastly increasing your intelligence? - Easily reach peak states for real, breakthrough spiritual experiences? Indeed and much more. This is not just another fuzzy New Age book about a magical, vague 'sixth sense'. Igniting the Sixth Sense deals with the magnetic sense that allows birds, whales, bees and many other animals to detect and use magnetic fields in ways that seem impossible for humans. Yet, we possess this same natural ability. What happens if you combine a modern human with an active, magnetic sensory? They display skills and abilities that seem, at times, super-human. Eric Pepin, the #1 spiritual Amazon bestselling author of Meditation within Eternity and The Handbook of the Navigator, takes you on a journey where you will discover: * How to access a larger memory beyond your own; the Akashic Records * Discover the method of magnetic prana attraction that makes the entire Universe react to who you are and what you want * How to enhance every mind, body and spiritual technique you practice. Including meditation, martial arts and more * The 15 minute miracle that gives you nearly unlimited amounts of energy * How to create a 'psychic buffer' so you aren't overwhelmed, harmed or influenced by the energy and thoughts of others * Learn the secret to programming your own energy field And that's just the beginning. There are 280 pages worth, packed with in-depth knowledge and effective techniques you can easily apply. It's all here, and it works.

    An expedition is mounted to the deepest parts of the Amazon in search of the unknown. A team of researchers travels down remote tributaries and treks through dense jungle terrain in search of creatures long thought to be extinct. It isn’t long before they realize that certain remnants of the primeval past have managed to survive into current times.

    The text begins with a discussion of the real number system as a complete ordered field. (Dedekind's construction is now treated in an appendix to Chapter I.) The topological background needed for the development of convergence, continuity, differentiation and integration is provided in Chapter 2. There is a new section on the gamma function, and many new and interesting exercises are included. This text is part of the Walter Rudin Student Series in Advanced Mathematics.

    The invaluable companion to the new edition of the bestselling How to Measure Anything This companion workbook to the new edition of the insightful and eloquent How to Measure Anything walks readers through sample problems and exercises in which they can master and apply the methods discussed in the book.The book explains practical methods for measuring a variety of intangibles, including approaches to measuring customer satisfaction, organizational flexibility, technology risk, technology ROI, and other problems in business, government, and not-for-profits.Companion to the revision of the bestselling How to Measure AnythingProvides chapter-by-chapter exercises Written by industry leader Douglas Hubbard Written by recognized expert Douglas Hubbard--creator of Applied Information Economics--How to Measure Anything Workbook illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.