Book Image

Data Analysis Using SQL and Excel - Second Edition

By : Gordon S. S. Linoff
Book Image

Data Analysis Using SQL and Excel - Second Edition

By: Gordon S. S. Linoff

Overview of this book

Data Analysis Using SQL and Excel, 2nd Edition 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. Data Analysis Using SQL and Excel, 2nd Edition 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.
Table of Contents (18 chapters)
Free Chapter
1
Foreword
17
EULA

Lessons Learned

Time is important for understanding the universe and time is important for data analysis. In databases, times and dates have six components: years, months, days, hours, minutes, and seconds. In addition, a time zone might also be attached. The structure is complicated, but within one database, times and dates tend to be from one time zone and at the same level of precision.

As with other data types, dates and times need to be validated. The most important validations are checking the range of values and verifying that dates have no extraneous time component.

Analyzing dates starts with the values and the counts themselves. Looking at counts and aggregations over time is informative, whether the number of customers, the order size, or the amount spent. Seasonal patterns appear in the data, further showing what customers are really doing. Many businesses have weekly cycles. For instance, stops may be higher on weekdays than on weekends. Comparisons at the day level show...