Data Analysis Using SQL and Excel
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Beschreibung
Learn to perform sophisticated data analysis using SQL and Excel
SQL 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.
This 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.
The 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.
As 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.
With more than 100 pages of new material, the fully revised second edition of Data Analysis Using SQL and Excel enables you to:
* Understand core analytic techniques that work with SQL and Excel
* Analyze and interpret data in a table
* Present data professionally in Excel charts
* Apply the chi-square measure and other important statistical techniques in both SQL and Excel
* Understand best practices for SQL queries, with a chapter devoted to performance
* Use survival analysis to understand time-to-event problems, both for single events and for repeated events
* Use market basket analysis to understand purchasing behavior
* Identify the analytic approach that gets the result you're looking for
* Avoid common pitfalls
* Maximize the value of the data you have about your customers and your business
The companion website includes datasets for all examples in the book as well as related Excel spreadsheets.
www.wiley.com/go/dataanalysisusingsqlandexcel2e
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.
Autor*in
Gordon S. Linoff
Themen in »Data Analysis Using SQL and Excel«
Computer Science Data Analysis Data Mining & Knowledge Discovery Data Mining u. Knowledge Discovery Datenanalyse Excel Informatik SQL Statistics Statistik