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SQL Server 2017 Developer???s Guide

SQL Server 2017 Developer???s Guide

3.6 (5)
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SQL Server 2017 Developer???s Guide

SQL Server 2017 Developer???s Guide

3.6 (5)

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 (19 chapters)
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1
Introduction to SQL Server 2017

Intermediate statistics – associations

In the previous chapter, you learned about discrete statistics methods for getting information about the distribution of discrete and continuous variables. In a data science project, the next typical step is to check for the associations between pairs of variables.

When checking for the associations between pairs of variables, you have three possibilities:

  • Both variables are discrete
  • Both variables are continuous
  • There is one discrete and one continuous variable

Besides dealing with two variables only, this section also introduces linear regression, one of the most important statistical methods, where you model a single response (or dependent) variable with a regression formula that includes one or more predictor (or independent) variables.

Altogether, you will learn about the following in this section:

  • Chi-squared test of the independence...

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