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

The power of SQL comes from its being a descriptive language rather than a procedural language. A SQL query describes the result set, rather than the specific algorithms used to create it. Database engines support many different algorithms, so even a simple query can have multiple implementation choices, as complicated as out-of-memory parallel algorithms or as simple as just scanning all the rows in the table as if it were a file.

From a performance perspective, indexes are the most important component of relational databases. Indexes do not change SQL queries at all, because the optimizer does the work of figuring out how to use them. For the problems discussed in this book, B-tree indexes are the most appropriate. Other types of indexes exist, such as inverted indexes for text, R-trees for spatial data, and even more esoteric types.

Despite the many implementations of relational databases, there are common themes for writing good queries that perform well. Of course...