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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
- Veröffentlichungsjahr: 2018
SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you.
This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment.
You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm.
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Data Science with SQL Server Quick Start Guide
Copyright © 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Amey VarangaonkarAcquisition Editor: Reshma RamanContent Development Editor:Roshan KumarTechnical Editor:Sushmeeta JenaCopy Editor: Safis EditingProject Coordinator:Hardik BhindeProofreader: Safis EditingIndexer:Aishwarya GangawaneGraphics:Jason MonteiroProduction Coordinator:Shraddha Falebhai
First published: August 2018
Production reference: 1300818
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78953-712-3
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Contributors
About the author
Dejan Sarka, MCT and Microsoft Data Platform MVP, is an independent trainer and consultant who focuses on the development of database and business intelligence applications. Besides projects, he spends about half his time on training and mentoring. He is the founder of the Slovenian SQL Server and .NET Users Group. He is the main author or co author of many books about databases and SQL Server. The last three books before this one were published by Packt, and their titles were SQL Server 2016 Developer's Guide,SQL Server 2017 Integration Services Cookbook, and SQL Server 2016 Developer's Guide. Dejan Sarka has also developed many courses and seminars for Microsoft, SolidQ, and Pluralsight.
About the reviewer
Tomaz Kastrun is an SQL Server developer and data scientist with more than 15 years, experience in the fields of business warehousing, development, ETL, database administration, and also data analysis and machine learning. He is a Microsoft Data Platform MVP, a blogger, and a frequent speaker at community and Microsoft events. When he is not working, drinking coffee, or riding a fixed-gear bike, he enjoys spending time with his daughter, Rubi.
Packt is searching for authors like you
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
Table of Contents
Title Page
Copyright and Credits
Data Science with SQL Server Quick Start Guide
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Writing Queries with T-SQL
Before starting – installing SQL Server
SQL Server setup 
Core T-SQL SELECT statement elements
The simplest form of the SELECT statement
Joining multiple tables
Grouping and aggregating data
Advanced SELECT techniques
Introducing subqueries
Window functions
Common table expressions
Finding top n rows and using the APPLY operator
Summary
Introducing R
Obtaining R
Your first line R of code in R
Learning the basics of the R language
Using R data structures
Summary
Getting Familiar with Python
Selecting the Python environment
Writing your first python code
Using functions, branches, and loops
Organizing the data
Integrating SQL Server and ML
Summary
Data Overview
Getting familiar with a data science project life cycle
Ways to measure data values
Introducing descriptive statistics for continuous variables
Calculating centers of a distribution
Measuring the spread
Higher population moments
Using frequency tables to understand discrete variables
Showing associations graphically
Summary
Data Preparation
Handling missing values
Creating dummies
Discretizing continuous variables
Equal width discretization
Equal height discretization
Custom discretization
The entropy of a discrete variable
Advanced data preparation topics
Efficient grouping and aggregating in T-SQL
Leveraging Microsoft scalable libraries in Python
Using the dplyr package in R
Summary
Intermediate Statistics and Graphs
Exploring associations between continuous variables
Measuring dependencies between discrete variables
Discovering associations between continuous and discrete variables
Expressing dependencies with a linear regression formula
Summary
Unsupervised Machine Learning
Installing ML services (In-Database) packages 
Performing market-basket analysis
Finding clusters of similar cases
Principal components and factor analyses
Summary
Supervised Machine Learning
Evaluating predictive models
Using the Naive Bayes algorithm
Predicting with logistic regression
Trees, forests, and more trees
Predicting with T-SQL
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
The book will give you a jump-start in data science with Microsoft SQL Server and in-database Machine Learning Services (ML Services). It covers all stages of a data science project, from business and data understanding through data overview, data preparation, and modeling, to using algorithms, model evaluation, and deployment. The book shows how to use the engines and languages that come with SQL Server, including ML Services with R, Python, and Transact-SQL (T-SQL). You will find useful code examples in all three languages mentioned. The book also shows which algorithms to use for which tasks, and briefly explains each algorithm.
Who this book is for
SQL Server only started to fully support data science with its two latest versions, 2016 and 2017. Therefore, SQL Server is not widely used for data science yet. However, there are professionals from the worlds of SQL Server and data science who are interested in using SQL Server and ML Services for their projects. Therefore, this book is intended for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects.
What this book covers
Chapter 1, Writing Queries with T-SQL, gives a brief overview of T-SQL queries. It introduces all of the important parts of the mighty SELECT statement and focuses on analytical queries.
Chapter 2, Introducing R, introduces the second language in this book, R. R has been supported in SQL Server since version 2016. In order to use it properly, you have to understand the language constructs and data structures.
Chapter 3, Getting Familiar with Python, gives an overview of the second most popular data science language, Python. As a more general language, Python is probably even more popular than R. Lately, Python has been catching up with R in the data science field.
Chapter 4, Data Overview, deals with understanding data. You can use introductory statistics and basic graphs for this task. You will learn how to perform a data overview in all three languages used in this book.
Chapter 5, Data Preparation, teaches you how to work with the data that you get from your business systems and from data warehouses, which is typically not suited for direct use in a machine learning project. You need to add derived variables, deal with outliers and missing values, and more.
Chapter 6, Intermediate Statistics and Graphs, starts with the real analysis of the data. You can use intermediate-level statistical methods and graphs for the beginning of your advanced analytics journey.
Chapter 7, Unsupervised Machine Learning, explains the algorithms that do not use a target variable. It is like fishing in the mud - you try and see if some meaningful information can be extracted from your data. The most common undirected techniques are clustering, dimensionality reduction, and affinity grouping, also known as basket analysis or association rules.
Chapter 8, Supervised Machine Learning, deals with the algorithms that need a target variable. Some of the most important directed techniques include classification and estimation. Classification means examining a new case and assigning it to a predefined discrete class, for example, assigning keywords to articles and assigning customers to known segments. Next is estimation, where you try to estimate the value of a continuous variable of a new case. You can, for example, estimate the number of children or the family income. This chapter also shows you how you can evaluate your machine learning models and use them for predictions.
To get the most out of this book
In order to run the demo code associated with this book, you will need SQL Server 2017, SQL Server Management Studio, and Visual Studio 2017.
All of the information about the installation of the software needed to run the code is included in the first three chapters of the book.
Download the example code files
You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packtpub.com
.
Select the
SUPPORT
tab.
Click on
Code Downloads & Errata
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Enter the name of the book in the
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box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
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Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Data-Science-with-SQL-Server-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/DataSciencewithSQLServerQuickStartGuide_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."
A block of code is set as follows:
# R version and contributorsR.version.stringcontributors()
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
1 + 22 + 5 * 43 ^ 4
sqrt(81)
pi
Any command-line input or output is written as follows:
install.packages("RODBC")
library(RODBC)
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."
Get in touch
Feedback from our readers is always welcome.
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Reviews
Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!
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Writing Queries with T-SQL
This book is intended for any SQL Server developer or database administrator (DBA) who wants to start working in the data science field. In addition, this book is also aimed at existing data scientists who want to start using SQL Server with related services and tools. I will use, and show examples in, three programming languages in this book: Transact-SQL (or T-SQL), R, and Python. Therefore, it makes sense to start with a brief introduction of the three languages. This is what the first three chapters are about. If you are already a SQL Server developer, proficient in writing T-SQL queries, you can simply skip the first chapter. If you are already working with R, skip the second chapter. If you are familiar with Python, please feel free to skip the third chapter.
This first chapter is not a comprehensive reference guide to T-SQL; I will focus on the mighty SELECT statement only, the statement you need to use immediately when your data is located in a SQL Server database. However, besides the basic clauses, I will also explain advanced techniques, such as window functions, common table expressions, and the APPLY operator.
This chapter will cover the following points:
Core Transact-SQL SELECT statement elements
Advanced SELECT techniques
Before starting – installing SQL Server
If you don't have a SQL Server yet, you can use a free SQL Server Evaluation Edition or Developer Edition. You can download any of them from the SQL Server downloads site at https://www.microsoft.com/en-ca/sql-server/sql-server-downloads.
SQL Server setup
You just start SQL Server setup, and then from the Feature Selection page select the following:
Database Engine Services
Underneath
Machine Learning
(ML)
Services (In-Database)
With both languages,
R
and
Python
, selected, like you can see in the next screenshot
After that, all you need is client tools, and you can start writing the code. The following screenshot shows the SQL Server setup Feature Selection page with the appropriate features selected:
The next step is to install client tools. Of course, you need SQL Server Management Studio (SSMS). You can download it at https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017. If you are not familiar with this tool, please use the SSMS at https://docs.microsoft.com/en-us/sql/ssms/tutorials/tutorial-sql-server-management-studio?view=sql-server-2017 to learn the basics about this tool.
Finally, you need some demo data. I will mostly use the AdventureWorksDW2017 demo database in this book. You can find this demo database and other Microsoft samples at https://github.com/Microsoft/sql-server-samples/releases. I will point you to the appropriate sources when I use any other demo data in this book.
After you install everything mentioned so for, you are ready to start learning or renewing the knowledge of the T-SQL SELECT statement.
Core T-SQL SELECT statement elements
You probably already know that the most important SQL statement is the mighty SELECT statement you use to retrieve data from your databases. Every database developer knows the basic clauses and their usage:
SELECT
: Defines the columns returned, or a projection of all table columns
FROM
: Lists the tables used in the query and how they are associated, or joined
WHERE
: Filters the data to return only the rows that satisfy the condition in the predicate
GROUP BY
: Defines the groups over which the data is aggregated
HAVING
: Filters the data after the grouping with conditions that refer to aggregations
ORDER BY
: Sorts the rows returned to the client application
