图书简介

Introduction: Data Science, Many Skills \\ What is Data Science? \\ The Steps in Doing Data Science \\ The Skills Needed to Do Data Science \\ Chapter 1: About Data \\ Storing Data – Using Bits and Bytes \\ Combining Bytes into Larger Structures \\ Creating a Dataset in R \\ Chapter 2: Identifying Data Problems \\ Talking to Subject Matter Experts \\ Looking for the Exception \\ Exploring Risk and Uncertainty \\ Chapter 3: Getting Started with R \\ Installing R \\ Using R \\ Creating and Using Vectors \\ Chapter 4: Follow the Data \\ Understand Existing Data Sources \\ Data Models \\ Chapter 5: Rows and Columns \\ Creating Dataframes \\ Exploring Dataframes \\ Accessing Columns in a Dataframe \\ Chapter 6: Data Munging \\ Reading a CSV Text File \\ Removing Rows and Columns \\ Renaming Rows and Columns \\ Cleaning up the Elements \\ Sorting Dataframes \\ Chapter 7: Onward with RStudio \\ Using an Integrated Development Environment \\ Installing RStudio \\ Creating R Scripts \\ Chapter 8: What’s My Function? \\ Why Create and Use Functions \\ Creating Functions in R \\ Testing Functions \\ Installing a Package to Access a Function \\ Chapter 9: Beer, Farms, and Peas and the Use of Statistics \\ Historical perspective \\ Sampling a population \\ Understanding Descriptive Statistics \\ Using Descriptive Statistics \\ Using Histograms to Understand a Distribution \\ Normal Distributions \\ Chapter 10: Sample in a Jar \\ Sampling in R \\ Repeating our Sampling \\ Law of Large Numbers and the Central Limit Theorem \\ Comparing Two Samples \\ Chapter 11: Storage Wars \\ Importing Data Using RStudio \\ Accessing Excel Data \\ Accessing a Database \\ Comparing SQL and R for accessing a dataset \\ Accessing JSON Data \\ Chapter 12: Pictures vs Numbers \\ A Visualization Overview \\ Basic Plots in R \\ Using GGPLOT2 \\ More Advanced GGPLOT2 Visualizations \\ Chapter 13: Map Mash-Up \\ Creating Map Visualizations with GGPLOT2 \\ Showing Points on a Map \\ A Map Visualization Example \\ Chapter 14: Word Perfect \\ Reading in Text Files \\ Using the Text Mining Package \\ Creating Word Clouds \\ Chapter 15: Happy Words? \\ Sentiment Analysis \\ Other Uses of Text Mining \\ Chapter 16: Lining Up Our Models \\ What is a Model? \\ Linear Modeling \\ An Example - Car maintenance \\ Chapter 17: Hi Ho, Hi Ho - Data Mining We Go \\ Data Mining Overview \\ Association Rules Data \\ Association Rules Mining \\ Exploring How the Association Rules Algorithm Works \\ Chapter 18: What’s Your Vector, Victor? \\ Supervised and Unsupervised Learning \\ Supervised Learning via Support Vector Machines \\ SVM in R \\ Chapter 19: Shiny Apps \\ Creating Web Applications in R \\ Deploying the Application \\ Chapter 20: Big Data? Big Deal! \\ What is Big Data \\ The Tools For Big Data

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