This book is a primary review tool to prepare students for both the internal medicine clerkship and the end-rotation NBME shelf examination. This logical alternative to several limited-focus books blends a bullet-outline format students prefer in a review book with comprehensive paragraphs, as needed, for optimal preparation. Illustrations, charts, tables, graphs, mnemonics, and "Quick Hits" pearls for the clerkship speed and supplement learning. Ample content without superfluous detail enables students to readily evaluate and expand their knowledge of cardiology, pulmonary medicine, gastroenterology, hematology, neurology, endocrinology, rheumatology, nephrology, genitourinary disorders, fluids and electrolytes, dermatology, and musculoskeletal problems. A new section in this Second Edition presents 100 USMLE-style clinical vignette-based questions with answers. A color insert contains over thirty full-color images. A companion Website will offer the fully searchable text and color photographs.

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.

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.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what youâ??ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code