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)

Data exploration

Data exploration is an extremely important task in every data science or machine learning project. Without good knowledge of the data, we'll never succeed with our further predictive models. In this section, we will show you how to explore data using T-SQL queries, the SSIS Data Profiling Task, and a simple R function.

Exploring data using T-SQL

For simple data exploration, we can use T-SQL queries. Here, we will explore the uniqueness of values in columns where we estimate the uniqueness, a quality of reference between the SourceData.Contracts and SourceData.Actions tables, and also a rate of NULLs in several columns.

First of all, let's query both tables to obtain a sample of data and the structures...