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An important addition to the popular Step-Up series, Step-Up to Emergency Medicine uses the proven series format to provide a high-yield review of emergency medicine, ideal for preparing for clerkships/clinical rotations, end of rotation/shelf exams, and the USMLE Step 2. Clinical pearls, full-color illustrations, and "Quick Hits" provide essential information in an efficient, easy-to-remember manner, perfect for medical, physician assistant, and nurse practitioner students. Step-Up to Emergency Medicine delivers exactly what you need to know--both for exam preparation and for practical use in the evaluation and treatment of patients in the emergency department. Succinct, outline approach focuses on the core content you need to know. "Quick Hits" in the margins highlight highly testable topics. Clinical pearls help you identify clinical connections for handy retrieval at test time, and aid in the transition from basic science to clinical medicine. More than 100 full-color photographs and line drawings illustrate key concepts throughout. Easy-to-follow algorithms show examples of effective clinical reasoning, particularly for similar complaints. Numerous charts, tables, graphs, and mnemonics facilitate easy retention of essential material. 100 online questions with complete rationale for correct and incorrect answers, help prepare you for exams and clinical practice. Coverage includes every essential area of emergency medicine, including psychiatric, pediatric, and environmental emergencies; trauma, wounds, toxicology, and medicolegal considerations-as well as must-know information on relevant pathophysiology, clinical features, differential diagnoses, patient evaluation, therapy, and more.
An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.
Now includes Step 2 practice Questions and Answers, with explanations, for each chapter! Current, concise, and easy to use, the updated fifth edition of Step-Up to USMLE Step 2 CK makes essential information engaging and retainable, delivering the perfect exam prep for today’s busy medical student. Access just what you need to ace the USMLE Step 2 CK and make a confident transition to clinical practice with this high-yield, system-based review. This updated fifth edition of Step-Up to USMLE Step 2 CK helps you quickly memorize essential information and gives you the realistic test-taking practice to succeed. Full-color illustrations, “Quick Hits,” and mnemonics clarify must-know information on causes, risk factors, history and physical exam, lab studies and radiology, treatment, and complications for the most common diseases and disorders. USMLE-style study questions for each chapter mimic those found on the Step 2 exams, accompanied by complete explanations for maximum comprehension and test preparation.
Learn how to use R to turn raw data into insight, knowledge, and understanding. 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
This high-yield review book gives you exactly the help you need to succeed on your family medicine clerkship, the NBME Family Medicine Shelf Exam, and the ambulatory component of the USMLE Step 2 CK.
Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.
Step Up to the TOEFL®iBTis a skills-based textbook designed to address the needs of students who have not yet reached a language level to successfully prepare for the TOEFL® iBT. This volume does what no other textbook does: it helps intermediate-level students take a “step up” toward preparing themselves for the iBT by teaching and developing some of the grammar, vocabulary, and pronunciation skills necessary to do well on the test. Each of the eight units inStep Upaddresses one rhetorical function (ranging from chronology and sequences to developing ideas and paraphrasing) and includes: twoGrammar You Can Usetopics that strengthen students' receptive and productive language threeVocabulary You Needsections highlighting common language functions seen on the iBT twoSpeaking Clearlysections that focus on improving comprehensibility and fluent delivery skill-building exercises that practice a language point through high-interest reading, writing, listening, and speaking activities iBT practice exercises that focus on a language point in ways similar to those on the actual test (including the integrated speaking and writing tasks) but at this intermediate-level of competency Step Up Noteswith useful hints and tips about improving performance on the iBT
Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn Understand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
From the unique perspective of the executive who discovered them, One Step Closer reveals how Brad Delson’s college internship was a catalyst for a group of young musical visionaries, led by Mike Shinoda, which gave rise to a band that survived countless rejections, exceeded everyone’s expectations but their own, and became the voice of a generation. This against-all-odds story chronicles the early days of Linkin Park, from their first demo and Whisky a Go Go performance as Xero, through their tireless efforts to perfect their iconic sound and the discovery of Chester Bennington. Jeff Blue was there when no one else believed—first as their publisher, then as their A&R guy. This is his memoir of that incredible journey. Riveting and inspiring, One Step Closer is a testament to perseverance, as well as a detailed behind-the-scenes account of the building of a dream and what it takes to make it.