Die zweite Auflage von Data Analysis Using SQL and Excel zeigt, wie man diese beiden beliebten Werkzeuge der Datenabfrage und -analyse am besten nutzt, um anspruchsvolle Datenanalysen ohne komplexe, teuere Data-Mining-Tools einsetzen zu müssen. Sie lernen zunächst die grundlegenden statistischen Verfahren kennen, erfahren, wo und warum die einzelnen Analysen eingesetzt werden, und erhalten dann das notwendige Rüstzeug, um diese Analysen mit SQL und Excel durchzuführen. Jedes Beispiel enthält Beispiel-Codes, Tipps und Warnhinweise. Die begleitende Website bietet Datensätze und Excel-Tabellen…mehr
Die zweite Auflage von Data Analysis Using SQL and Excel zeigt, wie man diese beiden beliebten Werkzeuge der Datenabfrage und -analyse am besten nutzt, um anspruchsvolle Datenanalysen ohne komplexe, teuere Data-Mining-Tools einsetzen zu müssen. Sie lernen zunächst die grundlegenden statistischen Verfahren kennen, erfahren, wo und warum die einzelnen Analysen eingesetzt werden, und erhalten dann das notwendige Rüstzeug, um diese Analysen mit SQL und Excel durchzuführen. Jedes Beispiel enthält Beispiel-Codes, Tipps und Warnhinweise. Die begleitende Website bietet Datensätze und Excel-Tabellen zum besseren Verständnis des Themas. - Vermittelt umfassend die notwendigen Grundlagen, um wichtige Analyseverfahren zu verstehen. - Zeigt Analyseansätze, um genau die benötigten Ergebnisse zu erzielen. - Erläutert, wie Analysen mit SQL und Excel durchgeführt werden.
In diesem Buch erfahren Sie, wie Sie mit bereits bekannten Tools aussagekräftige Ergebnisse erzielen.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
GORDON S. LINOFF has been working with databases for more decades than he cares to admit. He starting learning about SQL by memorizing the SQL 92 standard while leading a development team (at the now-defunct Thinking Machines Corporation) writing the first high-performance database focused on the complex queries needed for decision support. After that endeavor, Gordon co-founded Data Miners in 1998, a consulting practice devoted to data mining, analytics, and big data. A constant theme in his work is data?and often data in relational databases. His SQL skills have only gotten stronger over the years. In 2014, he was the top contributor to Stack Overflow, the leading question-and-answer-site for technical questions. His other books include the bestselling Data Mining Techniques, Third Edition; Mastering Data Mining; and Mining the Web?which focus on data mining and analysis. This book follows on the popularity of the first edition, with a practical focus on how to actually get and interpret results.
Inhaltsangabe
Foreword xxxiii Introduction xxxvii Chapter 1 A Data Miner Looks at SQL 1 Databases, SQL, and Big Data 2 Picturing the Structure of the Data 6 Picturing Data Analysis Using Dataflows 16 SQL Queries 21 Subqueries and Common Table Expressions Are Our Friends 36 Lessons Learned 47 Chapter 2 What's in a Table? Getting Started with Data Exploration 49 What Is Data Exploration? 50 Excel for Charting 51 Sparklines 65 What Values Are in the Columns? 68 More Values to Explore-Min, Max, and Mode 79 Exploring String Values 81 Exploring Values in Two Columns 86 From Summarizing One Column to Summarizing All Columns 90 Lessons Learned 96 Chapter 3 How Different Is Different? 97 Basic Statistical Concepts 98 How Different Are the Averages? 105 Sampling from a Table 110 Counting Possibilities 115 Ratios and Their Statistics 128 Chi-Square 132 What Months and Payment Types Have Unusual Affinities for Which Types of Products? 140 Lessons Learned 143 Chapter 4 Where Is It All Happening? Location, Location, Location 145 Latitude and Longitude 146 Census Demographics 160 Geographic Hierarchies 172 Mapping in Excel 188 Lessons Learned 194 Chapter 5 It's a Matter of Time 197 Dates and Times in Databases 198 Starting to Investigate Dates 204 How Long Between Two Dates? 218 Year-over-Year Comparisons 229 Counting Active Customers by Day 239 Simple Chart Animation in Excel 247 Lessons Learned 254 Chapter 6 How Long Will Customers Last? Survival Analysis to Understand Customers and Their Value 255 Background on Survival Analysis 256 The Hazard Calculation 260 Survival and Retention 269 Comparing Different Groups of Customers 280 Comparing Survival over Time 287 Important Measures Derived from Survival 293 Using Survival for Customer Value Calculations 298 Forecasting 308 Lessons Learned 314 Chapter 7 Factors Affecting Survival: The What and Why of Customer Tenure 315 Which Factors Are Important and When 316 Left Truncation 328 Time Windowing 336 Competing Risks 342 Before and After 353 Lessons Learned 366 Chapter 8 Customer Purchases and Other Repeated Events 367 Identifying Customers 368 RFM Analysis 393 Which Households Are Increasing Purchase Amounts Over Time? 404 Time to Next Event 416 Lessons Learned 420 Chapter 9 What's in a Shopping Cart? Market Basket Analysis 421 Exploring the Products 422 Products and Customer Worth 437 Product Geographic Distribution 448 Which Customers Have Particular Products? 451 Lessons Learned 463 Chapter 10 Association Rules and Beyond 465 Item Sets 466 The Simplest Association Rules 480 One-Way Association Rules 483 Two-Way Associations 489 Extending Association Rules 499 Lessons Learned 506 Chapter 11 Data Mining Models in SQL 507 Introduction to Directed Data Mining 508 Look-Alike Models 515 Lookup Model for Most Popular Product 522 Lookup Model for Order Size 528 Lookup Model for Probability of Response 534 Naive Bayesian Models (Evidence Models) 546 Lessons Learned 559 Chapter 12 The Best-Fit Line: Linear Regression Models 561 The Best-Fit Line 562 Measuring Goodness of Fit Using R2 581 Direct Calculation of Best-Fit Line Coefficients 584 Weighted Linear Regression 592 More Than One Input Variable 600 Lessons Learned 607 Chapter 13 Building Customer Signatures for Further Analysis 609 What Is a Customer Signature? 610 Designing Customer Signatures 617 Operations to Build Customer Signatures 622 Extracting Features 639 Summarizing Customer Behaviors 644 Lessons Learned 653 Chapter 14 Performance Is the Issue: Using SQL Effectively 655 Query Engines and Performance 656 Considerations When Thinking About Performance 660 Performance: Its Meaning and Measurement 663 Performance Improvement 101 665 Using Indexes Effectively 668 When OR Is a Bad Thing 683 Pros and Cons: Different Ways of Expressing the Same Thing 686 Window Functions 694 Lessons Learned 701 Appendix Equivalent Constructs Among Databases 703 Index 731
Foreword xxxiii Introduction xxxvii Chapter 1 A Data Miner Looks at SQL 1 Databases, SQL, and Big Data 2 Picturing the Structure of the Data 6 Picturing Data Analysis Using Dataflows 16 SQL Queries 21 Subqueries and Common Table Expressions Are Our Friends 36 Lessons Learned 47 Chapter 2 What's in a Table? Getting Started with Data Exploration 49 What Is Data Exploration? 50 Excel for Charting 51 Sparklines 65 What Values Are in the Columns? 68 More Values to Explore-Min, Max, and Mode 79 Exploring String Values 81 Exploring Values in Two Columns 86 From Summarizing One Column to Summarizing All Columns 90 Lessons Learned 96 Chapter 3 How Different Is Different? 97 Basic Statistical Concepts 98 How Different Are the Averages? 105 Sampling from a Table 110 Counting Possibilities 115 Ratios and Their Statistics 128 Chi-Square 132 What Months and Payment Types Have Unusual Affinities for Which Types of Products? 140 Lessons Learned 143 Chapter 4 Where Is It All Happening? Location, Location, Location 145 Latitude and Longitude 146 Census Demographics 160 Geographic Hierarchies 172 Mapping in Excel 188 Lessons Learned 194 Chapter 5 It's a Matter of Time 197 Dates and Times in Databases 198 Starting to Investigate Dates 204 How Long Between Two Dates? 218 Year-over-Year Comparisons 229 Counting Active Customers by Day 239 Simple Chart Animation in Excel 247 Lessons Learned 254 Chapter 6 How Long Will Customers Last? Survival Analysis to Understand Customers and Their Value 255 Background on Survival Analysis 256 The Hazard Calculation 260 Survival and Retention 269 Comparing Different Groups of Customers 280 Comparing Survival over Time 287 Important Measures Derived from Survival 293 Using Survival for Customer Value Calculations 298 Forecasting 308 Lessons Learned 314 Chapter 7 Factors Affecting Survival: The What and Why of Customer Tenure 315 Which Factors Are Important and When 316 Left Truncation 328 Time Windowing 336 Competing Risks 342 Before and After 353 Lessons Learned 366 Chapter 8 Customer Purchases and Other Repeated Events 367 Identifying Customers 368 RFM Analysis 393 Which Households Are Increasing Purchase Amounts Over Time? 404 Time to Next Event 416 Lessons Learned 420 Chapter 9 What's in a Shopping Cart? Market Basket Analysis 421 Exploring the Products 422 Products and Customer Worth 437 Product Geographic Distribution 448 Which Customers Have Particular Products? 451 Lessons Learned 463 Chapter 10 Association Rules and Beyond 465 Item Sets 466 The Simplest Association Rules 480 One-Way Association Rules 483 Two-Way Associations 489 Extending Association Rules 499 Lessons Learned 506 Chapter 11 Data Mining Models in SQL 507 Introduction to Directed Data Mining 508 Look-Alike Models 515 Lookup Model for Most Popular Product 522 Lookup Model for Order Size 528 Lookup Model for Probability of Response 534 Naive Bayesian Models (Evidence Models) 546 Lessons Learned 559 Chapter 12 The Best-Fit Line: Linear Regression Models 561 The Best-Fit Line 562 Measuring Goodness of Fit Using R2 581 Direct Calculation of Best-Fit Line Coefficients 584 Weighted Linear Regression 592 More Than One Input Variable 600 Lessons Learned 607 Chapter 13 Building Customer Signatures for Further Analysis 609 What Is a Customer Signature? 610 Designing Customer Signatures 617 Operations to Build Customer Signatures 622 Extracting Features 639 Summarizing Customer Behaviors 644 Lessons Learned 653 Chapter 14 Performance Is the Issue: Using SQL Effectively 655 Query Engines and Performance 656 Considerations When Thinking About Performance 660 Performance: Its Meaning and Measurement 663 Performance Improvement 101 665 Using Indexes Effectively 668 When OR Is a Bad Thing 683 Pros and Cons: Different Ways of Expressing the Same Thing 686 Window Functions 694 Lessons Learned 701 Appendix Equivalent Constructs Among Databases 703 Index 731
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