Data science for business with R:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles
Sage
[2022]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxxii, 389 Seiten Illustrationen |
| ISBN: | 9781544370453 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | DETAILED CONTENTS Instructor Preface Teaching Resources Introduction: Data Science, Many Skills What Is Data Science? The Steps in Doing Data Science The Skills Needed to Do Data Science Identifying Data ProbLems Additional Introductory Thoughts Case Study Overview: Customer Churn in the Airline Industry xiii xv xvii xviii xix xx xxii xxv xxvii Net Promoter Score xxviii Southeast and Its Regional Airline Partners xxviii The Data Available xxix Attribute Names xxix Chapter Challenges xxxii Sources xxxii Chapter 1 · Begin at the Beginning With R 1 Installing R 3 Using R 4 Creating and Using Vectors 5 Subsetting Vectors 8 The Command Console 10 Using an Integrated Development Environment 11 Installing RStudio 12 Creating R Scripts 13 Case Study: Calculating NPS 17 Chapter Challenges 19 Sources 19 R Functions Used in This Chapter 19 Chapter 2 · Rows and Columns 21 Creating Dataframes 24 Exploring Dataframes 27
viii Oata Science for Business With R Accessing CoLumns in a Dataframe 31 Case Study: Calculating NPS Using a Dataframe 34 Chapter Challenges 37 Sources 37 R Functions Used in This Chapter 38 Chapter 3 · Data Munging 39 Reading a CSV Text File 40 Removing Rows and Columns 44 Renaming Rows and Columns 46 Cleaning up the Elements 47 Sorting and Subsetting Dataframes 49 Tidyverse: An Introduction and How to Install the Package 51 Sorting and Subsetting Dataframes Using Tidyverse 53 Case Study: Reading, Cleaning, and Exploring a Survey Dataset 55 Chapter Challenges 59 Sources 60 R Functions Used in This Chapter 60 Chapter 4 · What s My Function? 61 Why Create and Use Functions? 62 Creating Functions in R 63 Defensive Coding 68 Installing a Package to Access a Function 70 Case Study: Creating and Using a Calculate NPS Function 72 Chapter Challenges 76 Sources 76 R Functions Used in This Chapter 77 Chapter 5 · Beer, Farms, Peas, and the Use of Statistics 79 Historical Perspective 80 Sampling a Population 82 Understanding Descriptive Statistics 82 Using Descriptive Statistics 84 Using Histograms to Understand a Distribution 88 Normal Distributions 91 Case Study: Exploring LTR Distributions 92 Chapter Challenges 95 Sources 95 R Functions Used in This Chapter 96
Detailed Contents Chapter 6 · Sample in a Jar Sampling in R 97 100 Repeating our Sampling 101 Law of Large Numbers and the Central Limit Theorem 103 Comparing Two Samples 107 Case Study: Analyzing the Impact of a New Treatment 112 Chapter Challenges 116 Sources 116 R Functions Used in This Chapter 117 Chapter 7 · Storage Wars 119 Importing Data Using RStudio 121 Accessing Excel Data 124 Working with Data From External Databases 129 Accessing a Database 130 Comparing SQL and R/Tidyverse for Accessing a Dataset 135 Accessing JSON Data 139 Case Study: Reading, Cleaning, and Exploring a Survey Dataset 145 Chapter Challenges 150 Sources 151 R Functions Used in This Chapter 151 Chapter 8 · Pictures Versus Numbers A Visualization Overview 153 155 Basic Plots in R 157 Using the ggplot2 Package 158 More-Advanced Visualizations 166 Case Study: Visualizing Key Attributes Related to NPS 171 Chapter Challenges 179 Sources 179 R Functions Used in This Chapter 180 Chapter 9 · MapMashup Creating Map Visualizations With ggplot2 181 183 Showing Points on a Map 192 Zooming Into a Subset of a Map 198 Case Study: Explore NPS by State and City 200 Chapter Challenges 204 Sources 204 R Functions Used in This Chapter 205 ix
X Data Science for Business With R Chapter 10 · Lining Up Our Models 207 What Is a Model? 208 Supervised and Unsupervised Machine Learning 208 Linear Modeling 210 An Example—Car Maintenance 212 Using the Caret Package 221 Partitioning into Training and Cross Validation Datasets 223 Using к-fold Cross Validation 228 Case Study: Building a Linear Model Using Survey Data 231 Chapter Challenges 236 Sources 236 ... R Functions Used in This Chapter Chapter 11 · What’s Your Vector, Victor? 237 239 More Supervised Learning 240 A Classification Example 240 Supervised Learning via Support Vector Machines 247 Support Vector Machines in R 250 Supervised Learning via Classification and Regression Trees 261 Case Study: Building Supervised Models From the Survey 266 Chapter Challenges 274 Sources 274 R Functions Used in This Chapter 275 Chapter 12 · Hi Ho, Hi Ho—Data Mining We Go 277 Data Mining Processes 279 Association Rules Data 280 Association Rules Mining 281 Exploring How the Association Rules Algorithm Works 287 Building Association Rules in R 288 Case Study: Exploring Association Rules Within the Survey 295 Chapter Challenges 300 Sources 301 R Functions Used in This Chapter 301 Chapter 13 · Word Perfect (Text Mining) 303 Reading-In Text Files 305 Creating Word Clouds Using the Quanteda Package 307 Exploring the Text via Sentiment Analysis 311
Detailed Contents Topic Modeling 314 Other Uses of Text Mining 318 Case Study: Connecting Topics to NPS 319 Chapter Challenges 332 Sources 332 R Functions Used in This Chapter 333 Chapter 14 · Shiny® Web Apps 335 Creating Web Applications in R 336 Deploying the Application 341 Case Study: Visualizing NPS by Key Attributes 347 Chapter Challenges 351 Sources 351 R Functions Used in This Chapter 351 Chapter 15 · Time for a Deep Dive 353 The Impact of Deep Learning 354 Deep Learning Is Supervised Learning 355 How Does Deep Learning Work? 356 Deep Learning in R—An Example 358 Deep Learning in R—An Image Analysis Example 365 Deep Learning in R—Using a Prebuilt Model 374 Case Study: Building Neural Networks From the Survey 378 Chapter Challenges 381 Sources 382 R Functions Used in This Chapter 383 Index 385 ХІ
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| adam_txt |
DETAILED CONTENTS Instructor Preface Teaching Resources Introduction: Data Science, Many Skills What Is Data Science? The Steps in Doing Data Science The Skills Needed to Do Data Science Identifying Data ProbLems Additional Introductory Thoughts Case Study Overview: Customer Churn in the Airline Industry xiii xv xvii xviii xix xx xxii xxv xxvii Net Promoter Score xxviii Southeast and Its Regional Airline Partners xxviii The Data Available xxix Attribute Names xxix Chapter Challenges xxxii Sources xxxii Chapter 1 · Begin at the Beginning With R 1 Installing R 3 Using R 4 Creating and Using Vectors 5 Subsetting Vectors 8 The Command Console 10 Using an Integrated Development Environment 11 Installing RStudio 12 Creating R Scripts 13 Case Study: Calculating NPS 17 Chapter Challenges 19 Sources 19 R Functions Used in This Chapter 19 Chapter 2 · Rows and Columns 21 Creating Dataframes 24 Exploring Dataframes 27
viii Oata Science for Business With R Accessing CoLumns in a Dataframe 31 Case Study: Calculating NPS Using a Dataframe 34 Chapter Challenges 37 Sources 37 R Functions Used in This Chapter 38 Chapter 3 · Data Munging 39 Reading a CSV Text File 40 Removing Rows and Columns 44 Renaming Rows and Columns 46 Cleaning up the Elements 47 Sorting and Subsetting Dataframes 49 Tidyverse: An Introduction and How to Install the Package 51 Sorting and Subsetting Dataframes Using Tidyverse 53 Case Study: Reading, Cleaning, and Exploring a Survey Dataset 55 Chapter Challenges 59 Sources 60 R Functions Used in This Chapter 60 Chapter 4 · What's My Function? 61 Why Create and Use Functions? 62 Creating Functions in R 63 Defensive Coding 68 Installing a Package to Access a Function 70 Case Study: Creating and Using a Calculate NPS Function 72 Chapter Challenges 76 Sources 76 R Functions Used in This Chapter 77 Chapter 5 · Beer, Farms, Peas, and the Use of Statistics 79 Historical Perspective 80 Sampling a Population 82 Understanding Descriptive Statistics 82 Using Descriptive Statistics 84 Using Histograms to Understand a Distribution 88 Normal Distributions 91 Case Study: Exploring LTR Distributions 92 Chapter Challenges 95 Sources 95 R Functions Used in This Chapter 96
Detailed Contents Chapter 6 · Sample in a Jar Sampling in R 97 100 Repeating our Sampling 101 Law of Large Numbers and the Central Limit Theorem 103 Comparing Two Samples 107 Case Study: Analyzing the Impact of a New Treatment 112 Chapter Challenges 116 Sources 116 R Functions Used in This Chapter 117 Chapter 7 · Storage Wars 119 Importing Data Using RStudio 121 Accessing Excel Data 124 Working with Data From External Databases 129 Accessing a Database 130 Comparing SQL and R/Tidyverse for Accessing a Dataset 135 Accessing JSON Data 139 Case Study: Reading, Cleaning, and Exploring a Survey Dataset 145 Chapter Challenges 150 Sources 151 R Functions Used in This Chapter 151 Chapter 8 · Pictures Versus Numbers A Visualization Overview 153 155 Basic Plots in R 157 Using the ggplot2 Package 158 More-Advanced Visualizations 166 Case Study: Visualizing Key Attributes Related to NPS 171 Chapter Challenges 179 Sources 179 R Functions Used in This Chapter 180 Chapter 9 · MapMashup Creating Map Visualizations With ggplot2 181 183 Showing Points on a Map 192 Zooming Into a Subset of a Map 198 Case Study: Explore NPS by State and City 200 Chapter Challenges 204 Sources 204 R Functions Used in This Chapter 205 ix
X Data Science for Business With R Chapter 10 · Lining Up Our Models 207 What Is a Model? 208 Supervised and Unsupervised Machine Learning 208 Linear Modeling 210 An Example—Car Maintenance 212 Using the Caret Package 221 Partitioning into Training and Cross Validation Datasets 223 Using к-fold Cross Validation 228 Case Study: Building a Linear Model Using Survey Data 231 Chapter Challenges 236 Sources 236 . R Functions Used in This Chapter Chapter 11 · What’s Your Vector, Victor? 237 239 More Supervised Learning 240 A Classification Example 240 Supervised Learning via Support Vector Machines 247 Support Vector Machines in R 250 Supervised Learning via Classification and Regression Trees 261 Case Study: Building Supervised Models From the Survey 266 Chapter Challenges 274 Sources 274 R Functions Used in This Chapter 275 Chapter 12 · Hi Ho, Hi Ho—Data Mining We Go 277 Data Mining Processes 279 Association Rules Data 280 Association Rules Mining 281 Exploring How the Association Rules Algorithm Works 287 Building Association Rules in R 288 Case Study: Exploring Association Rules Within the Survey 295 Chapter Challenges 300 Sources 301 R Functions Used in This Chapter 301 Chapter 13 · Word Perfect (Text Mining) 303 Reading-In Text Files 305 Creating Word Clouds Using the Quanteda Package 307 Exploring the Text via Sentiment Analysis 311
Detailed Contents Topic Modeling 314 Other Uses of Text Mining 318 Case Study: Connecting Topics to NPS 319 Chapter Challenges 332 Sources 332 R Functions Used in This Chapter 333 Chapter 14 · Shiny® Web Apps 335 Creating Web Applications in R 336 Deploying the Application 341 Case Study: Visualizing NPS by Key Attributes 347 Chapter Challenges 351 Sources 351 R Functions Used in This Chapter 351 Chapter 15 · Time for a Deep Dive 353 The Impact of Deep Learning 354 Deep Learning Is Supervised Learning 355 How Does Deep Learning Work? 356 Deep Learning in R—An Example 358 Deep Learning in R—An Image Analysis Example 365 Deep Learning in R—Using a Prebuilt Model 374 Case Study: Building Neural Networks From the Survey 378 Chapter Challenges 381 Sources 382 R Functions Used in This Chapter 383 Index 385 ХІ |
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| title | Data science for business with R |
| title_auth | Data science for business with R |
| title_exact_search | Data science for business with R |
| title_exact_search_txtP | Data science for business with R |
| title_full | Data science for business with R Jeffrey S. Saltz, Jeffrey Morgan Stanton |
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| title_full_unstemmed | Data science for business with R Jeffrey S. Saltz, Jeffrey Morgan Stanton |
| title_short | Data science for business with R |
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| topic_facet | Data Science R Programm Betriebswirtschaftslehre Lehrbuch |
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