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Hands-On Data Science with SQL Server 2017
Hands-On Data Science with SQL Server 2017

Hands-On Data Science with SQL Server 2017: Perform end-to-end data analysis to gain efficient data insight

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Profile Icon Marek Chmel Profile Icon Vladimír Mužný
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NZ$71.99
Paperback Nov 2018 506 pages 1st Edition
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NZ$14.99 NZ$57.99
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NZ$71.99
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Arrow left icon
Profile Icon Marek Chmel Profile Icon Vladimír Mužný
Arrow right icon
NZ$71.99
Paperback Nov 2018 506 pages 1st Edition
eBook
NZ$14.99 NZ$57.99
Paperback
NZ$71.99
Subscription
Free Trial
eBook
NZ$14.99 NZ$57.99
Paperback
NZ$71.99
Subscription
Free Trial

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Hands-On Data Science with SQL Server 2017

SQL Server 2017 as a Data Science Platform

SQL Server is a relational database management system, but through the time it has evolved into much more. Right now, it's a very complex set of tools that work together to bring you superior experience while working with your data. Originally, SQL Server was not even written for the Windows operating system. The genesis of the SQL Server started with the OS/2 operating system in 1989 (it's a product that is nearly 30 years old). The first version of SQL Server available for Windows came in 1993. It was a version 4.21a.

Through various releases, SQL Server has introduced numerous features that can be utilized not only for regular database operations and production deployment of SQL Server, but also for the data science domain.

We will be covering the following topics in this chapter:

  • SQL Server server evolution
  • SQL Server Services...

Technical requirements

SQL Server 2017 Evaluation Edition.
SQL Server Management Studio (version 17.7 was used at the time of writing the book as the latest edition).

SQL Server evolution

Let's get started with a brief overview of the features that are currently available with SQL and then skim over a brief history of SQL Server.

What's available in the pack?

The main core of the SQL Server is a DBMS (database management system), which is used to store the data in the databases. Through time and the various versions available, Microsoft has added other services as a part of the SQL Server that we can deploy and use for other data-related tasks. While these services are not necessary and required for database operations, they add value to your installation.

Without specifying any particular order for the services that expand the feature set of SQL Server beyond plain DBMS, the...

SQL Server Services and their use with data science

We will first start with SQL Server Integration Services. SQL Server analysis will be covered later. We will also be discussing Reporting Services, Power BI Report Server and machine learning services.

SQL Server Integration Services

SQL Server Integration Services (SSIS) was first introduced with SQL Server 2005, which was a major release, where Microsoft revised most of the Sybase code into Microsoft code. Numerous changes did happen in the background of the SQL Server and three main business intelligence services were introduced, SSIS being one of them. The concept of integration service used with SQL Server was, however, introduced with SQL Server 7.0, known as Data...

Summary

There are numerous services that make up the whole SQL Server environment, which you can run either on-premise or in the cloud. The core of the SQL Server is a database management system, which stores the data and provides us with the data operation engine, but the data also has to come in the SQL Server and it needs to be analyzed and represented. Therefore, we have companion services, such as Integration Services, which provide us the ETL solution for importing and cleaning the data before storing it on the SQL Server, Analysis Services, which is used for multidimensional modeling and data mining, and Reporting Services, which is a visualization part of the whole stack. No matter whether you choose the on-premise approach or the cloud services, there are always a number of offerings from the SQL Server that you can utilize for data science.

...
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Key benefits

  • Perform end-to-end data analysis—from exploration to visualization
  • Real-world examples, tasks, and interview queries to be a proficient data scientist
  • Understand how SQL is used for big data processing using HiveQL and SparkSQL

Description

SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features. Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples. By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs.

Who is this book for?

Hands-On Data Science with SQL Server 2017 is intended for data scientists, data analysts, and big data professionals who want to master their skills learning SQL and its applications. This book will be helpful even for beginners who want to build their career as data science professionals using the power of SQL Server 2017. Basic familiarity with SQL language will aid with understanding the concepts covered in this book.

What you will learn

  • Understand what data science is and how SQL Server is used for big data processing
  • Analyze incoming data with SQL queries and visualizations
  • Create, train, and evaluate predictive models
  • Make predictions using trained models and establish regular retraining courses
  • Incorporate data source querying into SQL Server
  • Enhance built-in T-SQL capabilities using SQLCLR
  • Visualize data with Reporting Services, Power View, and Power BI
  • Transform data with R, Python, and Azure
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 29, 2018
Length: 506 pages
Edition : 1st
Language : English
ISBN-13 : 9781788996341
Vendor :
Microsoft
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Languages :
Concepts :
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Product Details

Publication date : Nov 29, 2018
Length: 506 pages
Edition : 1st
Language : English
ISBN-13 : 9781788996341
Vendor :
Microsoft
Category :
Languages :
Concepts :
Tools :

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Table of Contents

13 Chapters
Data Science Overview Chevron down icon Chevron up icon
SQL Server 2017 as a Data Science Platform Chevron down icon Chevron up icon
Data Sources for Analytics Chevron down icon Chevron up icon
Data Transforming and Cleaning with T-SQL Chevron down icon Chevron up icon
Data Exploration and Statistics with T-SQL Chevron down icon Chevron up icon
Custom Aggregations on SQL Server Chevron down icon Chevron up icon
Data Visualization Chevron down icon Chevron up icon
Data Transformations with Other Tools Chevron down icon Chevron up icon
Predictive Model Training and Evaluation Chevron down icon Chevron up icon
Making Predictions Chevron down icon Chevron up icon
Getting It All Together - A Real-World Example Chevron down icon Chevron up icon
Next Steps with Data Science and SQL Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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