An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout.
An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jeffrey S. Saltz is an Associate Professor at Syracuse University in the School of Information Studies and Director of the school¿s Master¿s of Science program in Applied Data Science. His research and teaching focus on helping organizations leverage information technology and data for competitive advantage. Specifically, his current research focuses on the socio-technical aspects of data science projects, such as how to coordinate and manage data science teams. In order to stay connected to the "real world", Dr. Saltz consults with clients ranging from professional football teams to Fortune 500 organizations. Prior to becoming a professor, Dr. Saltz¿s two decades of industry experience focused on leveraging emerging technologies and data analytics to deliver innovative business solutions. In his last corporate role, at JPMorgan Chase, he reported to the firm¿s Chief Information Officer and drove technology innovation across the organization. Jeff also held several other key technology management positions at the company, including CTO and Chief Information Architect. He also served as Chief Technology Officer and Principal Investor at Goldman Sachs, where he helped incubate technology start-ups. He started his career as a programmer, project leader and consulting engineer with Digital Equipment Corp. Dr. Saltz holds a B.S. degree in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania, and a PhD in Information Systems from the New Jersey Institute of Technology.
Inhaltsangabe
Preface About the Authors 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 Data Set 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 Exploring 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 Data Set Accessing JSON Data Chapter 12 Pictures Versus Numbers A Visualization Overview Basic Plots in R Using ggplot2 More Advanced ggplot2 Visualizations Chapter 13 Map Mashup 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 Support Vector Machines in R Chapter 19 Shiny® Web Apps Creating Web Applications in R Deploying the Application Chapter 20 Big Data? Big Deal! What Is Big Data? The Tools for Big Data Index
Preface About the Authors 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 Data Set 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 Exploring 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 Data Set Accessing JSON Data Chapter 12 Pictures Versus Numbers A Visualization Overview Basic Plots in R Using ggplot2 More Advanced ggplot2 Visualizations Chapter 13 Map Mashup 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 Support Vector Machines in R Chapter 19 Shiny® Web Apps Creating Web Applications in R Deploying the Application Chapter 20 Big Data? Big Deal! What Is Big Data? The Tools for Big Data Index
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