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)

Creating CLR aggregations

As Chapter 5, Data Exploration and Statistics with T-SQL described, SQL Server provides a rich set of very feasible built-in aggregate functions to fulfill our statistical needs. However, for deeper insight into our data, the list of built-in functions could be insufficient. This section is a walkthrough example of how to create our own custom aggregation using C# and the CLR aggregate SQL Server object.

Example goal and assignment

In Chapter 5, Data Exploration and Statistics with T-SQL, we learned about variance as well as the rate of parameter scattering. Let's revisit the following formula:

We can generalize the preceding formula. If we replace the power of 2 with (almost) any number, k...