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)

Summary

Data exploration is an important part of every data science project; without these insights into data, we will mostly make blind and inaccurate estimations that will not meet our intentions and needs. We learned about statistics made with T-SQL in three parts, going from the easiest to the most difficult.

In the first section of the chapter, we described almost all aggregate functions used for descriptive statistics. By using functions such as COUNT or SUM, we became familiar with their particular purposes and the roles they are playing in data science, and we also observed their common behavior. The GROUP BY and GROUP BY GROUPING SETS clauses of SELECT statements were also described in detail.

In the second section of the chapter, we introduced ranking functions and their functionality. With the help of ranking functions, we also learned about framing and windowing, helping...