Book Image

SQL Server 2017 Developer???s Guide

Book Image

SQL Server 2017 Developer???s Guide

Overview of this book

Microsoft SQL Server 2017 is a milestone in Microsoft's data platform timeline, as it brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. This book prepares you for advanced topics by starting with a quick introduction to SQL Server 2017's new features. Then, it introduces you to enhancements in the Transact-SQL language and new database engine capabilities before switching to a different technology: JSON support. You will take a look at the security enhancements and temporal tables. Furthermore, the book focuses on implementing advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. Toward the end of the book, you'll be introduced to R and how to use the R language with Transact-SQL for data exploration and analysis. You'll also learn to integrate Python code into SQL Server and graph database implementations as well as the deployment options on Linux and SQL Server in containers for development and testing. By the end of this book, you will be armed to design efficient, high-performance database applications without any hassle.
Table of Contents (25 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Free Chapter
1
Introduction to SQL Server 2017
Index

Chapter 14. Data Exploration and Predictive Modeling with R

Using the R language inside SQL Server gives us the opportunity to get knowledge out of data. We introduced R and R support in SQL Server in the previous chapter, and this chapter demonstrates how you can use R for advanced data exploration, statistical analysis, and predictive modeling, way beyond the possibilities offered by using the T-SQL language only.

You will start with intermediate statistics: exploring associations between two discrete and two continuous variables, and one discrete and one continuous variable. You will also learn about linear regression, where you explain the values of a dependent continuous variable with a linear regression formula using one or more continuous input variables.

The second section of this chapter starts by introducing advanced multivariate data mining and machine learning methods. You will learn about methods that do not use a target variable, or so-called undirected methods.

In the third part...