Educators will find information on how to teach students to read based on evidence from a broad base of effective, well-designed research. Topics have been updated and added to better reflect current thinking in the field and address issues that have come to national and international attention for a number of reasons, including the recently released U.S. National Reading Panel report. The editors maintain a balance among theory, research, and effective classroom practice without presenting a formulaic view of good instruction or overly theoretical discussions in which practical applications of research findings are not adequately explored. The 17 chapters focus on research related to early reading instruction, phonemic awareness, comprehension, and many other topics. Each chapter concludes with "Questions for Discussion"; to encourage reflection on the topics discussed. Teacher educators will find this volume to be a valuable tool for preservice teacher preparation as well as graduate level courses. The professional development community, school administrators, and policymakers will also find it to be an indispensable resource as they seek to implement programs consistent with rapidly emerging legislative and policy mandates.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

     -Byte, September 1995 I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me! I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up. -Charles Long If you think you're a really good programmer... read [Knuth's] Art of Computer Programming... You should definitely send me a resume if you can read the whole thing. -Bill Gates It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers. -Jonathan Laventhol This first volume in the series begins with basic programming concepts and techniques, then focuses more particularly on information structures-the representation of information inside a computer, the structural relationships between data elements and how to deal with them efficiently. Elementary applications are given to simulation, numerical methods, symbolic computing, software and system design. Dozens of simple and important algorithms and techniques have been added to those of the previous edition. The section on mathematical preliminaries has been extensively revised to match present trends in research. Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP), http: //msp.org

    Mixing investment instruments and capital markets with the theoretical detail on evaluating investments and opportunities to satisfy risk-return objectives along with how investment practice and theory is influenced by globalization. The material is intended to be rigorous and empirical yet not overly quantitative. Reilly/Brown provides the best foundation, used extensively by professionals, organizations, and schools across the country. A great source for those with both a theoretical and practical need for investment expertise.

    This brand new book from Ross Morrison McGill, bestselling author of 100 Ideas for Secondary Teachers: Outstanding Lessons and Teacher Toolkit, is packed full of practical ideas that will help teachers refine the key elements of their profession. Mark. Plan. Teach. shows how each stage of the teaching process informs the next, building a cyclical framework that underpins everything that teachers do.With teachers' workload at record levels and teacher recruitment and retention the number one issue in education, ideas that really work and will help teachers not only survive but thrive in the classroom are in demand. Every idea in Mark. Plan. Teach. can be implemented by all primary and secondary teachers at any stage of their career and will genuinely improve practice. The ideas have been tried and tested and are supported by evidence that explains why they work, including current educational research and psychological insights from Dr Tim O'Brien, leading psychologist and Visiting Fellow at UCL Institute of Education.Mark. Plan. Teach. will enable all teachers to maximise the impact of their teaching and, in doing so, save time, reduce workload and take back control of the classroom.

    The book begins with an introduction to the language of statistics and then covers descriptive statistics and inferential statistics. Throughout, the author offers readers:- Difficulty Rating Index for each chapter′s material- Tips for doing and thinking about a statistical technique- Top tens for everything from the best ways to create a graph to the most effective techniques for data collection- Steps that break techniques down into a clear sequence of procedures- SPSS tips for executing each major statistical technique- Practice exercises at the end of each chapter, followed by worked out solutions.The book concludes with a statistical software sampler and a description of the best Internet sites for statistical information and data resources. Readers also have access to a website for downloading data that they can use to practice additional exercises from the book. Students and researchers will appreciate the book′s unhurried pace and thorough, friendly presentation.

    The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to take a hard pass on Knuth's brilliant but impenetrable theories and the dense multi-page proofs you'll find in most textbooks, this is the book for you. This fully-illustrated and engaging guide makes it easy for you to learn how to use algorithms effectively in your own programs.Grokking Algorithms is a disarming take on a core computer science topic. In it, you'll learn how to apply common algorithms to the practical problems you face in day-to-day life as a programmer. You'll start with problems like sorting and searching. As you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression or artificial intelligence. Whether you're writing business software, video games, mobile apps, or system utilities, you'll learn algorithmic techniques for solving problems that you thought were out of your grasp. For example, you'll be able to:Write a spell checker using graph algorithmsUnderstand how data compression works using Huffman codingIdentify problems that take too long to solve with naive algorithms, and attack them with algorithms that give you an approximate answer insteadEach carefully-presented example includes helpful diagrams and fully-annotated code samples in Python. By the end of this book, you will know some of the most widely applicable algorithms as well as how and when to use them.

    All the while, they're boosting decoding and spelling skills, broadening vocabulary, and becoming better, more fluent readers.

    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

    The authors guide individuals and teams on an inspiring journey to show them how to persevere through challenges, overcome obstacles, and create success together.Stick Together follows Coach David, a high school basketball coach looking to motivate his team for the new season. The team members are given sticks with words written on them and tasked with a number of missions:To find another player with the same word written on their stick To explain why that word is important for a team to be their best To render their sticks unbreakable As the players work together to complete their tasks, they discover how to make their team stronger and create an unbreakable bond. Perfect for student athletes and teams in all industries including business, education, healthcare, and nonprofit, and for readers of all ages, Stick Together will resonate with anyone looking to improve their team performance and excel in a group environment.

    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.

    The authors identify 10 qualities, attributes, and skills that help to explain Lincoln's effectiveness, despite seemingly insurmountable odds:1. Implementing and sustaining a mission and vision with focused and profound clarity2. Communicating ideas effectively with precise and straightforward language3. Building a diverse and competent team to successfully address the mission4. Engendering trust, loyalty, and respect through humility, humor, and personal example5. Leading and serving with emotional intelligence and empathy6. Exercising situational competence and responding appropriately to implement effective change7. Rising beyond personal and professional trials through tenacity, persistence, resilience, and courage8. Exercising purposeful visibility9. Demonstrating personal growth and enhanced competence as a lifetime learner, willing to reflect on and expand ideas10. Believing that hope can become a realityChapters devoted to each element explore the historical record of Lincoln's life and actions, then discuss the implications for modern educators. End-of-chapter exercises provide a structure for reflection, analysis of current behaviors, and guidance for future work, so that readers can create their own path to success--inspired by the example of one of the greatest leaders of all time.

    It’s about markets.The past twenty-five years have witnessed a remarkable shift in how we get the stuff we want. If you’ve ever owned a business, rented an apartment, or shopped online, you’ve had a front-row seat for this revolution-in-progress. Breakthrough companies like Amazon and Uber have disrupted the old ways and made the economy work better—all thanks to technology.At least that’s how the story of the modern economy is usually told. But in this lucid, wry book, Ray Fisman and Tim Sullivan show that the revolution is bigger than tech: it is really a story about the transformation of markets. From the auction theories that power Google’s ad sales algorithms to the models that online retailers use to prevent internet fraud, even the most high-tech modern businesses are empowered by theory first envisioned by economists.And we’re all participants in this revolution. Every time you book a room on Airbnb, hire a car on Lyft, or click on an ad, you too are reshaping our social institutions and our lives.The Inner Lives of Markets is necessary reading for the modern world: it reveals the blueprint for how we work, live, and shop, and offers wisdom for how to do it better.

    Never before in history have we been able to acquire, record, communicate, and use information in so many different forms. Never before have we had access to such vast quantities of data of every kind. This revolution goes far beyond the limitless content that fills our lives, because information also underlies our understanding of ourselves, the natural world, and the universe. It is the key that unites fields as different as linguistics, cryptography, neuroscience, genetics, economics, and quantum mechanics. And the fact that information bears no necessary connection to meaning makes it a profound puzzle that people with a passion for philosophy have pondered for centuries.Table of ContentsLECTURE 1The Transformability of Information 4LECTURE 2Computation and Logic Gates 17LECTURE 3Measuring Information 26LECTURE 4Entropy and the Average Surprise 34LECTURE 5Data Compression and Prefix-Free Codes 44LECTURE 6Encoding Images and Sounds 57LECTURE 7Noise and Channel Capacity 69LECTURE 8Error-Correcting Codes 82LECTURE 9Signals and Bandwidth 94LECTURE 10Cryptography and Key Entropy 110LECTURE 11Cryptanalysis and Unraveling the Enigma 119LECTURE 12Unbreakable Codes and Public Keys 130LECTURE 13What Genetic Information Can Do 140LECTURE 14Life’s Origins and DNA Computing 152LECTURE 15Neural Codes in the Brain 169LECTURE 16Entropy and Microstate Information 185LECTURE 17Erasure Cost and Reversible Computing 198LECTURE 18Horse Races and Stock Markets 213LECTURE 19Turing Machines and Algorithmic Information 226LECTURE 20Uncomputable Functions and Incompleteness 239LECTURE 21Qubits and Quantum Information 253LECTURE 22Quantum Cryptography via Entanglement 266LECTURE 23It from Bit: Physics from Information 281LECTURE 24The Meaning of Information 293

    You will have numerous opportunities to practice statistical techniques through learning checks, examples, demonstrations, and problems. Exam preparation is made easy with a student companion website that provides tutorials, crossword puzzles, flashcards, learning objectives, and more!