Book Image

Hands-On Data Science with SQL Server 2017

By : Marek Chmel, Vladimír Mužný
Book Image

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)

Getting It All Together - A Real-World Example

The previous chapters in this book taught us how to use particular technologies or features within or outside of SQL Server, but this chapter is going to be different. We will use some of the technologies and features described earlier, and we will also build an entire predictive solution. This chapter will guide us through the following sections:

  • Assignment and preparation: First of all, we need to know the topic of our real-world example. We also need to prepare the environment for further work. This section is all about the assignment of the project and its preparation tasks.
  • Data exploration: Our projects already have data saved into a relational database. In this section, we will explore what the data contains, how reliable the data quality is, and we'll develop a statistical summary of the data
  • Data transformation: Based...