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

This chapter showed that T-SQL is not the only language or environment used in data science for data transformation. All of the technology shown in this chapter deserves—and has—its own publications. The goal of this chapter was to show technologies beyond T-SQL and to offer inspiration for further studies.

The first section of this chapter was an introduction to several types of transformations often needed in the data science domain. We also learned about categorization, standardization, and missing-value imputations in the form of terms and formulas.

The knowledge obtained in the first section was used to introduce SQL Server Integration Services in the second section. Here, we created a simple package to show how the development is done. During the development of the Data Flow task, we learned about some transformations and their usage.

The section on...