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Hands-On Data Science with SQL Server 2017

By : Marek Chmel , Vladimír Mužný

Buy this Book

Hands-On Data Science with SQL Server 2017

By: Marek Chmel , Vladimír Mužný

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Data Science Overview

Introducing data science

Data science domains

Summary

SQL Server 2017 as a Data Science Platform

Technical requirements

SQL Server evolution

SQL Server Services and their use with data science

Summary

Data Sources for Analytics

Technical requirements

Getting data from databases

Importing flat files

Working with XML data

Working with JSON

External data with PolyBase

Summary

Data Transforming and Cleaning with T-SQL

Technical requirements

The need for data transformation

Database architectures for data transformations

Transforming data

Denormalizing data

Using views and stored procedures

Performance considerations

Summary

Questions

Data Exploration and Statistics with T-SQL

Technical requirements

T-SQL aggregate queries

Ranking, framing, and windowing

Running aggregates

Summary

Questions

Custom Aggregations on SQL Server

Technical requirements

Overview of SQLCLR

Creating CLR aggregations

Limitations and performance considerations

Summary

Questions

Data Visualization

Technical requirements

Data visualization – preparation phase

Power BI Report Server

SQL Server Reporting Services

Summary

Data Transformations with Other Tools

Technical requirements

Categorization, missing values, and normalization

Using Integration Services for data transformation

Using R for data transformation

Using Data Factory for data transformation

Summary

Questions

Predictive Model Training and Evaluation

Technical requirements

Preparing SQL Server

Creating data structures

Deploying, training, and evaluating a predictive model

Summary

Questions

Making Predictions

Technical requirements

Reading models from a database

Submitting values to an external script

Using the PREDICT keyword

Making the predictive model self-training

Summary

Questions

Getting It All Together - A Real-World Example

Technical requirements

Assignment and preparation

Data exploration

Data transformation

Training and using predictive models for estimations

Summary

Questions

Next Steps with Data Science and SQL

Data science next steps

Next steps with SQL Server

Data science in the cloud

Summary

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Data Sources for Analytics

In this chapter, we will review various sources of data that we can import and process in SQL Server for any analytical and data science techniques. This data can come from other database systems, flat files, application-specific files such as Excel, and web sources, among others. In regards to data structure, we can consider the data to be imported as structured, semi-structured, or unstructured. Based on the source and type of data, we have different tools in place that we can use to store the data in an SQL Server database.

We will cover the following topics in this chapter: