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Hands-On Data Science with SQL Server 2017

Hands-On Data Science with SQL Server 2017

By : Marek Chmel , Vladimír Mužný
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Hands-On Data Science with SQL Server 2017

Hands-On Data Science with SQL Server 2017

By: Marek Chmel , Vladimír Mužný

Overview of this book

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.
Table of Contents (14 chapters)
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Data Sources for Analytics

In this chapter, we will review various sources of data that we can import and process in SQL Server for any analytical and data science techniques. This data can come from other database systems, flat files, application-specific files such as Excel, and web sources, among others. In regards to data structure, we can consider the data to be imported as structured, semi-structured, or unstructured. Based on the source and type of data, we have different tools in place that we can use to store the data in an SQL Server database.

We will cover the following topics in this chapter:

  • Getting data from databases
  • Importing flat files
  • Working with XML data
  • Working with JSON
  • External data with PolyBase
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