This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    From the classroom to the courtroom and from financial markets to supermarkets, Mlodinow's intriguing and illuminating look at how randomness, chance, and probability affect our daily lives will intrigue, awe, and inspire.

    Readerswill find information on frequency distributions; mean, median, and mode; range, variance, and standard deviation;probability; and more.-Emphasizes Microsoft Excel for number-crunching and computationsDownload a sample chapter.

    Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

    ServSafe Manager, 7th edition, with exam answer sheetThe ServSafe Manager Book is ideal for one- or two-day classroom instruction helping students prepare to take the ServSafe Food Protection Manager Certification Exam.It covers critical principles including: personal hygiene, cross contamination, time and temperature, receiving and storage, food safety management systems, training hourly employees, and more.Meets all requirements:ServSafe classroom training is accepted in all 50 states for regulatory requirements up to 16 hours.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

    It's compelling, it's straightforward, and it can change your life. Zombie Economics is for anyone in the midst of financial uncertainty, a place where carelessness and timidity will cost you. From the creeping spread of unpaid bills to the lumbering advance of creditors, Zombie Economics confronts the biggest threats to your personal economy, takes aim, and then takes them down. Specific chapters include: A Basement Full of Ammo Saving yourself by saving money They'll Eat the Fat Ones First Using fitness as a financial asset Shooting Dad in the Head Ending your relationships with the financially infected With simple, easy-to-use techniques for identifying-and eliminating-your financial weak spots, Zombie Economics turns victims into survivors. Watch a Video"

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    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.

    Jones and George are dedicated to the challenge of "Making It Real" for students. The authors present management in a way that makes its relevance obvious even to students who might lack exposure to a "real-life" management context. This is accomplished thru a diverse set of examples, and the unique, and most popular feature of the text, the "Manager as a Person" Chapter 2. This chapter discusses managers as real people with their own personalities, strengths, weaknesses, opportunities, and problems and this theme is carried thru the remaining chapters. This text also discusses the importance of management competencies--the specific set of skills, abilities, and experiences that gives one manager the ability to perform at a higher level than another in a specific context. The themes of diversity, ethics, globalization, and information technology are integrated throughout.

    Ignatavicius and M. Linda Workman cover all the latest trends, evidence-based treatment guidelines, and additional updated information needed for safe clinical practice in medical-surgical nursing. This seventh edition features an expanded emphasis on patient safety and NCLEX? Examination preparation, new ties to the QSEN priorities for patient safety, and a greater alignment with the language and focus of clinical practice. A new chapter on evidence-based practice and a wealth of effective online learning tools help solidify your mastery of medical-surgical nursing.

    It provides the background needed to understand related drugs currently on the market, as well as drugs yet to be released. In simplifying a complex subject, this text focuses on the essentials of pharmacology. Large print is used to show need-to-know information, and small print is used for nice to know material. At the end of each chapter, a summary of major nursing implications helps in applying the material to real-world situations. This edition includes a new companion CD-ROM featuring NCLEX(R) examination-style review questions, a variety of electronic calculators, and animations depicting drug mechanisms and effects.Uses a prototype drug approach that places a strong emphasis on understanding over memorization - equipping students with the knowledge to learn not only about related drugs currently on the market, but also about those drugs that will be released once the student begins practice.Summaries of Major Nursing Implications at the end of each chapter provide an in-depth look at assessment, implementation, and ongoing evaluations.Utilizes large print for essential information and small print for nice-to-know information to help both faculty and students focus their limited classroom and study time on understanding the essentials.Concise drug summary tables present detailed information on individual drugs, including class, generic and trade names, dosages, routes, and indications.Key Points at the end of each chapter summarize content in a bulleted format to help students review important concepts.Prototype drug discussions employ a clear and consistent format with separate headings for Mechanism of Action; Pharmacologic Effects; Pharmacokinetics; Therapeutic Uses; Adverse Effects; Drug Interactions; and Preparations, Dosage, and Administration.An attractive full-color design adds visual interest, highlights key information, and facilitates student learning.Drugs for Multiple Sclerosis and Drugs for Hemophilia chapters.Drugs for Erectile Dysfunction and Benign Prostatic Hyperplasia chapter covers newsworthy drugs such as Viagra and Levitra.Special Interest Topics boxes on current issues in pharmacology, such as Medication-Overuse Headache: Too Much of a Good Thing and Face Time with Botox.Adult Immunization appendix summarizes the latest information on immunizations.Numerous new illustrations show drug mechanisms and effects, and depict topics such as histologic changes in Alzheimer's disease and the movement of drugs following GI absorption.