Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

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

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

One of the main concerns for digital photographers today is asset management: how to file, find, protect, and re-use their photos. The best solutions can be found in The DAM Book, our bestselling guide to managing digital images efficiently and effectively. Anyone who shoots, scans, or stores digital photographs is practicing digital asset management (DAM), but few people do it in a way that makes sense. In this second edition, photographer Peter Krogh -- the leading expert on DAM -- provides new tools and techniques to help professionals, amateurs, and students: Understand the image file lifecycle: from shooting to editing, output, and permanent storage Learn new ways to use metadata and key words to track photo files Create a digital archive and name files clearly Determine a strategy for backing up and validating image data Learn a catalog workflow strategy, using Adobe Bridge, Camera Raw, Adobe Lightroom, Microsoft Expression Media, and Photoshop CS4 together Migrate images from one file format to another, from one storage medium to another, and from film to digital Learn how to copyright images To identify and protect your images in the marketplace, having a solid asset management system is essential. The DAM Book offers the best approach.

This volume contains 73 papers, presenting the state of the art in computer-aided design in control systems (CADCS). The latest information and exchange of ideas presented at the Symposium illustrates the development of computer-aided design science and technology within control systems. The Proceedings contain six plenary papers and six special invited papers, and the remainder are divided into five themes: CADCS packages; CADCS software and hardware; systems design methods; CADCS expert systems; CADCS applications, with finally a discussion on CADCS in education and research.