文件名称：Mastering.Data.Analysis.with.R.1783982020
文件大小：7.58MB
文件格式：PDF
更新时间：2018-10-25 07:48:27
Data Analysis R
Gain sharp insights into your data and solve real-world data science problems with R―from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples. Table of Contents Chapter 1: Hello, Data! Chapter 2: Getting Data from the Web Chapter 3: Filtering and Summarizing Data Chapter 4: Restructuring Data Chapter 5: Building Models (authored by Renata Nemeth and Gergely Toth) Chapter 6: Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) Chapter 7: Unstructured Data Chapter 8: Polishing Data Chapter 9: From Big to Small Data Chapter 10: Classification and Clustering Chapter 11: Social Network Analysis of the R Ecosystem Chapter 12: Analyzing Time-series Chapter 13: Data Around Us Chapter 14: Analyzing the R Community