{"product_id":"data-analysis-using-sql-and-excel-isbn-9781119021438","title":"Data Analysis Using SQL and Excel","description":"\u003cb\u003eA practical guide to data mining using SQL and Excel\u003c\/b\u003e  \u003cp\u003e\u003ci\u003eData Analysis Using SQL and Excel, 2nd Edition\u003c\/i\u003e shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the \"where\" and \"why\" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2\/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Analysis Using SQL and Excel, 2nd Edition\u003c\/i\u003e shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUnderstand core analytic techniques that work with SQL and Excel\u003c\/li\u003e \u003cli\u003eEnsure your analytic approach gets you the results you need\u003c\/li\u003e \u003cli\u003eDesign and perform your analysis using SQL and Excel\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Analysis Using SQL and Excel, 2nd Edition\u003c\/i\u003e shows you how to best use the tools you already know to achieve expert results.\u003c\/p\u003eDie 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 mussen. Sie lernen zunachst die grundlegenden statistischen Verfahren kennen, erfahren, wo und warum die einzelnen Analysen eingesetzt werden, und erhalten dann das notwendige Rustzeug, um diese Analysen mit SQL und Excel durchzufuhren. Jedes Beispiel enthalt Beispiel-Codes, Tipps und Warnhinweise. Die begleitende Website bietet Datensatze und Excel-Tabellen zum besseren Verstandnis des Themas.\u003cbr\u003e - Vermittelt umfassend die notwendigen Grundlagen, um wichtige Analyseverfahren zu verstehen.\u003cbr\u003e - Zeigt Analyseansatze, um genau die benotigten Ergebnisse zu erzielen.\u003cbr\u003e - Erlautert, wie Analysen mit SQL und Excel durchgefuhrt werden.\u003cbr\u003e \u003cbr\u003e In diesem Buch erfahren Sie, wie Sie mit bereits bekannten Tools aussagekraftige Ergebnisse erzielen.\u003cbr\u003e \u003cbr\u003e \u003cbr\u003e \u003cp\u003eForeword xxxiii\u003c\/p\u003e \u003cp\u003eIntroduction xxxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 A Data Miner Looks at SQL 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDatabases, SQL, and Big Data 2\u003c\/p\u003e \u003cp\u003ePicturing the Structure of the Data 6\u003c\/p\u003e \u003cp\u003ePicturing Data Analysis Using Dataflows 16\u003c\/p\u003e \u003cp\u003eSQL Queries 21\u003c\/p\u003e \u003cp\u003eSubqueries and Common Table Expressions Are Our Friends 36\u003c\/p\u003e \u003cp\u003eLessons Learned 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 What’s in a Table? Getting Started with Data Exploration 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Data Exploration? 50\u003c\/p\u003e \u003cp\u003eExcel for Charting 51\u003c\/p\u003e \u003cp\u003eSparklines 65\u003c\/p\u003e \u003cp\u003eWhat Values Are in the Columns? 68\u003c\/p\u003e \u003cp\u003eMore Values to Explore—Min, Max, and Mode 79\u003c\/p\u003e \u003cp\u003eExploring String Values 81\u003c\/p\u003e \u003cp\u003eExploring Values in Two Columns 86\u003c\/p\u003e \u003cp\u003eFrom Summarizing One Column to Summarizing All Columns 90\u003c\/p\u003e \u003cp\u003eLessons Learned 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 How Different Is Different? 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Statistical Concepts 98\u003c\/p\u003e \u003cp\u003eHow Different Are the Averages? 105\u003c\/p\u003e \u003cp\u003eSampling from a Table 110\u003c\/p\u003e \u003cp\u003eCounting Possibilities 115\u003c\/p\u003e \u003cp\u003eRatios and Their Statistics 128\u003c\/p\u003e \u003cp\u003eChi-Square 132\u003c\/p\u003e \u003cp\u003eWhat Months and Payment Types Have Unusual Affinities for Which Types of Products? 140\u003c\/p\u003e \u003cp\u003eLessons Learned 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Where Is It All Happening? Location, Location, Location 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLatitude and Longitude 146\u003c\/p\u003e \u003cp\u003eCensus Demographics 160\u003c\/p\u003e \u003cp\u003eGeographic Hierarchies 172\u003c\/p\u003e \u003cp\u003eMapping in Excel 188\u003c\/p\u003e \u003cp\u003eLessons Learned 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 It’s a Matter of Time 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDates and Times in Databases 198\u003c\/p\u003e \u003cp\u003eStarting to Investigate Dates 204\u003c\/p\u003e \u003cp\u003eHow Long Between Two Dates? 218\u003c\/p\u003e \u003cp\u003eYear-over-Year Comparisons 229\u003c\/p\u003e \u003cp\u003eCounting Active Customers by Day 239\u003c\/p\u003e \u003cp\u003eSimple Chart Animation in Excel 247\u003c\/p\u003e \u003cp\u003eLessons Learned 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 How Long Will Customers Last? Survival Analysis to Understand Customers and Their Value 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBackground on Survival Analysis 256\u003c\/p\u003e \u003cp\u003eThe Hazard Calculation 260\u003c\/p\u003e \u003cp\u003eSurvival and Retention 269\u003c\/p\u003e \u003cp\u003eComparing Different Groups of Customers 280\u003c\/p\u003e \u003cp\u003eComparing Survival over Time 287\u003c\/p\u003e \u003cp\u003eImportant Measures Derived from Survival 293\u003c\/p\u003e \u003cp\u003eUsing Survival for Customer Value Calculations 298\u003c\/p\u003e \u003cp\u003eForecasting 308\u003c\/p\u003e \u003cp\u003eLessons Learned 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Factors Affecting Survival: The What and Why of Customer Tenure 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhich Factors Are Important and When 316\u003c\/p\u003e \u003cp\u003eLeft Truncation 328\u003c\/p\u003e \u003cp\u003eTime Windowing 336\u003c\/p\u003e \u003cp\u003eCompeting Risks 342\u003c\/p\u003e \u003cp\u003eBefore and After 353\u003c\/p\u003e \u003cp\u003eLessons Learned 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Customer Purchases and Other Repeated Events 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentifying Customers 368\u003c\/p\u003e \u003cp\u003eRFM Analysis 393\u003c\/p\u003e \u003cp\u003eWhich Households Are Increasing Purchase Amounts Over Time? 404\u003c\/p\u003e \u003cp\u003eTime to Next Event 416\u003c\/p\u003e \u003cp\u003eLessons Learned 420\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 What’s in a Shopping Cart? Market Basket Analysis 421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring the Products 422\u003c\/p\u003e \u003cp\u003eProducts and Customer Worth 437\u003c\/p\u003e \u003cp\u003eProduct Geographic Distribution 448\u003c\/p\u003e \u003cp\u003eWhich Customers Have Particular Products? 451\u003c\/p\u003e \u003cp\u003eLessons Learned 463\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Association Rules and Beyond 465\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eItem Sets 466\u003c\/p\u003e \u003cp\u003eThe Simplest Association Rules 480\u003c\/p\u003e \u003cp\u003eOne-Way Association Rules 483\u003c\/p\u003e \u003cp\u003eTwo-Way Associations 489\u003c\/p\u003e \u003cp\u003eExtending Association Rules 499\u003c\/p\u003e \u003cp\u003eLessons Learned 506\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Data Mining Models in SQL 507\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to Directed Data Mining 508\u003c\/p\u003e \u003cp\u003eLook-Alike Models 515\u003c\/p\u003e \u003cp\u003eLookup Model for Most Popular Product 522\u003c\/p\u003e \u003cp\u003eLookup Model for Order Size 528\u003c\/p\u003e \u003cp\u003eLookup Model for Probability of Response 534\u003c\/p\u003e \u003cp\u003eNaive Bayesian Models (Evidence Models) 546\u003c\/p\u003e \u003cp\u003eLessons Learned 559\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 The Best-Fit Line: Linear Regression Models 561\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Best-Fit Line 562\u003c\/p\u003e \u003cp\u003eMeasuring Goodness of Fit Using R\u003csup\u003e2\u003c\/sup\u003e 581\u003c\/p\u003e \u003cp\u003eDirect Calculation of Best-Fit Line Coefficients 584\u003c\/p\u003e \u003cp\u003eWeighted Linear Regression 592\u003c\/p\u003e \u003cp\u003eMore Than One Input Variable 600\u003c\/p\u003e \u003cp\u003eLessons Learned 607\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Building Customer Signatures for Further Analysis 609\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is a Customer Signature? 610\u003c\/p\u003e \u003cp\u003eDesigning Customer Signatures 617\u003c\/p\u003e \u003cp\u003eOperations to Build Customer Signatures 622\u003c\/p\u003e \u003cp\u003eExtracting Features 639\u003c\/p\u003e \u003cp\u003eSummarizing Customer Behaviors 644\u003c\/p\u003e \u003cp\u003eLessons Learned 653\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Performance Is the Issue: Using SQL Effectively 655\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eQuery Engines and Performance 656\u003c\/p\u003e \u003cp\u003eConsiderations When Thinking About Performance 660\u003c\/p\u003e \u003cp\u003ePerformance: Its Meaning and Measurement 663\u003c\/p\u003e \u003cp\u003ePerformance Improvement 101 665\u003c\/p\u003e \u003cp\u003eUsing Indexes Effectively 668\u003c\/p\u003e \u003cp\u003eWhen OR Is a Bad Thing 683\u003c\/p\u003e \u003cp\u003ePros and Cons: Different Ways of Expressing the Same Thing 686\u003c\/p\u003e \u003cp\u003eWindow Functions 694\u003c\/p\u003e \u003cp\u003eLessons Learned 701\u003c\/p\u003e \u003cp\u003eAppendix Equivalent Constructs Among Databases 703\u003c\/p\u003e \u003cp\u003eIndex 731\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eGORDON S. LINOFF\u003c\/b\u003e 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.  \u003c\/p\u003e\u003cp\u003eAfter 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.  \u003c\/p\u003e\u003cp\u003eHis other books include the bestselling \u003ci\u003eData Mining Techniques, Third Edition; Mastering Data Mining\u003c\/i\u003e; and \u003ci\u003eMining\u003c\/i\u003e \u003ci\u003ethe Web\u003c\/i\u003e—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.         \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLearn to perform sophisticated data analysis using SQL and Excel\u003c\/b\u003e   \u003c\/p\u003e\u003cp\u003eSQL is the essential language for querying databases, and Excel is the most popular tool for data presentation and analysis. Combined, they create a  powerful, accessible tool for business data analysis. Many important types of analysis do not require complex and expensive data mining tools.  The answers are on your desktop.  \u003c\/p\u003e\u003cp\u003eThis no-nonsense guide, written by a leading expert on business data mining, shows you how to design and perform sophisticated data analysis  using SQL and Excel. The highly regarded first edition has been revised to cover the newest enhancements to SQL and Excel, including new techniques  and real-world examples. This edition features the up-to-date information business managers and data analysts need.  \u003c\/p\u003e\u003cp\u003eThe book begins with the basics of SQL for data mining, Excel to present results, and simple ideas from statistics to understand your data.  Core analytic techniques are explained as you learn to run them on real data using Excel and SQL. The chapters progress from basic queries to increasingly  detailed applications as you learn why and when to perform specific types of analysis, how to design and perform them, and powerful ways of presenting the  results. Each step explains the business context, the technical approach, and the implementation in these familiar tools.  \u003c\/p\u003e\u003cp\u003eAs you progress, you'll discover the importance of geography, how to chart changes in data over time, how to use survival analysis to understand  customer tenure and churn, and the factors that affect survival. You will explore methods for analyzing customer purchases patterns, market basket analysis,  and association rules. Included are important data mining models in SQL, linear regression models, naive Bayesian models, information on building a customer  signature, methods for analyzing results, including cumulative gains charts and ROC charts, best practices for using SQL, and getting the best performance for  your queries.  \u003c\/p\u003e\u003cp\u003eWith more than 100 pages of new material, the fully revised second edition of \u003ci\u003eData Analysis Using SQL and Excel \u003c\/i\u003eenables you to:  \u003c\/p\u003e\u003cul\u003e \u003cli\u003e Understand core analytic techniques that work with SQL and Excel\u003c\/li\u003e \u003cli\u003e Analyze and interpret data in a table\u003c\/li\u003e \u003cli\u003e Present data professionally in Excel charts\u003c\/li\u003e \u003cli\u003e Apply the chi-square measure and other important statistical techniques in both SQL and Excel\u003c\/li\u003e \u003cli\u003e Understand best practices for SQL queries, with a chapter devoted to performance\u003c\/li\u003e \u003cli\u003e Use survival analysis to understand time-to-event problems, both for single events and for repeated events\u003c\/li\u003e \u003cli\u003e Use market basket analysis to understand purchasing behavior\u003c\/li\u003e \u003cli\u003e Identify the analytic approach that gets the result you're looking for\u003c\/li\u003e \u003cli\u003e Avoid common pitfalls\u003c\/li\u003e \u003cli\u003e Maximize the value of the data you have about your customers and your business\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003eThe companion website includes datasets for all examples in the book as well as related Excel spreadsheets.  \u003c\/p\u003e\u003cp\u003ewww.wiley.com\/go\/dataanalysisusingsqlandexcel2e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989023113445,"sku":"NP9781119021438","price":52.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119021438.jpg?v=1761782477","url":"https:\/\/k12savings.com\/products\/data-analysis-using-sql-and-excel-isbn-9781119021438","provider":"K12savings","version":"1.0","type":"link"}