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)

T-SQL aggregate queries

Data exploration and descriptive or comparative statistics are very important tasks that have to be done repeatedly and iteratively during every data science project. This gives us better insight into the data that we want to process throughout all projects. T-SQL aggregate queries are an important part of data exploration.

A T-SQL aggregate query is a kind of query that basically summarizes groups of records from underlying datasets and typically provides aggregated numeric values for each group of records generated from the dataset. Groups of records are not needed for every case or every assignment. Such aggregation queries give an aggregation of summarized values over whole underlying datasets.

The simplest aggregation query does not require grouping. With or without grouping, aggregate queries use special kinds of functions, called aggregate functions...