Before you get to the end of your leash, turn to this friendly and relatable reference that’s the next best thing to talking to a dog-owning friend who's seen it all. In Oh My Dog, animal rights activist Beth Ostrosky Stern has compiled tips and invaluable advice from experts—and from her own experience as dogowner—to sooth concerns, answer questions big and small, and help you and your dog get the most out of your relationship. From the moment you even consider getting a dog, to caring for your old friend when his puppy years are far behind him, Oh My Dog covers every angle of dog ownership, including: - Which breeds would be good match for me? - What do I look for in a vet? - How do I make sure our first night together is as stress-free as possible? - What activities will help me bond with my dog?- Is my dog showing sign of illness?- What should I know before I head to a doggie day care or park? - How do I read pet food labels? - What should I do in an emergency?Choc full of informative side bars, questionnaires, to-do lists, and much, much more, Oh My Dog is the answer-filled field guide for anybody who owns a dog or is considering getting one. Beth Ostrosky Stern

    Psychology of Learning for Instruction, Third Edition, focuses on the applications and implications of the learning theories. Using excellent examples ranging from primary school instruction to corporate training, this text combines the latest thinking and research to give readers the opportunity to explore the individual theories as viewed by the experts. Readers are encouraged to apply "reflective practice," which is designed to foster a critical and reflective mode of thinking when considering any particular approach to learning and instruction. Provides readers with the practical knowledge needed to apply learning theories to instruction. KEY TOPICS: This text addresses learning as it relates to behavior, cognition, development, biology, motivation and instruction. MARKET: Pre-service and in-service teachers, and educational psychologists.

    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

    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

    You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started--whether you're on Windows, OS X, or Linux--author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you'll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on plain text, CSV, HTML/XML, and JSONExplore data, compute descriptive statistics, and create visualizationsManage your data science workflow using DrakeCreate reusable tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelines using GNU ParallelModel data with dimensionality reduction, clustering, regression, and classification algorithms

    However, several revolutionary discoveries in the twentieth century revealed that there is a fundamental uncertainty at the heart of reality. Take a tour of chaos theory, the uncertainty principle, and read the saga of the South Pole and the Multiverse. Discover how uncertainty is the only certainty.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    The book includes charts with the recipes, instructions on keeping diets simple and balanced, guidelines on preparation, suggestions for finding ingredients, and how much to feed a dog by body weight. There are recipes for healthy adult dogs, as well as guidelines for puppies, senior dogs, and dogs with health conditions including pancreatitis, renal problems, gastric issues, allergies, heart disease, liver disease, and cancer.Tracing the history of feeding dogs, the author shows when commercial dog food rose and took hold of the market. She discusses canine nutritional needs and provides research on how home-prepared foods can meet pets' needs better than commercial, processed dog food. Written with thorough information for the seasoned raw feeder, this guide can also be easily followed by any newcomer to home-feeding.This revised edition includes new information on special care and feeding of pregnant, newborn, performance, and toy breed dogs as well as senior dog considerations and the safety of the raw food diet for dogs.From the Trade Paperback edition.

    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

    This book shows you how to claim your share. Over 200 pages of valuable metal detecting information designed to get you out there finding treasure on your very first outing.Finding treasure with a metal detector is real and doing it is simple and easy once you read this book. There are people finding incredible old coins made from gold and silver, valuable historical relics and old jewelry made from gold, silver and platinum. But you won't find these great treasures unless you know where and how to look. Metal Detecting: A Beginner's Guide shows you this and much more.Veteran detectorist and treasure enthusiast Mark Smith continues to provide great information to anyone interested in the great hobby of metal detecting. In his second book on the subject, he manages to answer the common questions that every novice has when they are thinking about getting started. From choosing the right machine to identifying your valuable treasure, Mark Smith covers these subjects and everything in between in an easy to understand way.While this metal detecting book may be geared towards the novice treasure hunter, there are plenty of choice tips that even experienced treasure hunters can pick up. Mark Smith reveals some of his best guarded metal detecting secrets in this metal detecting guide that puts more treasure in your find's pouch.Fully illustrated diagrams and real life pictures describe in detail the easiest ways to not only locate treasure, but safely recover it as well.Learn how to find more treasure by: understanding common metal detecting terminology, understanding which metal detectors work the best and where, understanding how and why a metal detector works, other equipment you will need, proper etiquette, what you can expect to find, why you should never throw anything away, how to identify your finds, how to identify jewelry, how to tell if it is real gold, how to metal detect with children, which recovery tools work the best, how to recover treasure, how to metal detect private property, how to identify unknown metal detecting finds, metal detecting creeks, rivers and lakes, pinpointing, making weak targets stronger, cleaning your finds, the best places to use your metal detector, selling your finds and more!What are you waiting for? Find out how you can maximize your treasure with this informative metal detecting book today.

    This popular Blueprints book has been refined and updated while keeping the concise, organized style and clinical high-yield content of previous editions. Features include USMLE-style questions and answers with full explanations; Key Points in every section; and a color-enhanced design that increases the usefulness of figures and tables.This edition's completely revised art program includes many additional illustrations. Each chapter in this edition ends with evidence-based references (journals) for students to do additional reading/research.

    The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes.Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.

    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

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    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