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SQL Server 2016 Developer's Guide

SQL Server 2016 Developer's Guide

By : Dejan Sarka, Radivojević, William Durkin
4.5 (8)
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SQL Server 2016 Developer's Guide

SQL Server 2016 Developer's Guide

4.5 (8)
By: Dejan Sarka, Radivojević, William Durkin

Overview of this book

Microsoft SQL Server 2016 is considered the biggest leap in the data platform history of the Microsoft, in the ongoing era of Big Data and data science. This book introduces you to the new features of SQL Server 2016 that will open a completely new set of possibilities for you as a developer. It prepares you for the more advanced topics by starting with a quick introduction to SQL Server 2016's new features and a recapitulation of the possibilities you may have already explored with previous versions of SQL Server. The next part introduces you to small delights in the Transact-SQL language and then switches to a completely new technology inside SQL Server - JSON support. We also take a look at the Stretch database, security enhancements, and temporal tables. The last chapters concentrate on implementing advanced topics, including Query Store, column store indexes, and In-Memory OLTP. You will finally be introduced to R and learn how to use the R language with Transact-SQL for data exploration and analysis. By the end of this book, you will have the required information to design efficient, high-performance database applications without any hassle.
Table of Contents (15 chapters)
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12
12. In-Memory OLTP Improvements in SQL Server 2016

Chapter 14. Data Exploration and Predictive Modeling with R in SQL Server

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 and for statistical analysis and predictive modeling, way beyond the possibilities offered by using T-SQL language only.

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

The second section of this chapter starts with 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...

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SQL Server 2016 Developer's Guide
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