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Table Of Contents

Data Science with SQL Server Quick Start Guide

By : Dejan Sarka

4 (1)

Buy this Book

Data Science with SQL Server Quick Start Guide

4 (1)

By: Dejan Sarka

Buy this Book

Overview of this book

SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm.

Preface

Who this book is for

What this book covers

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Free Chapter

Writing Queries with T-SQL

Before starting – installing SQL Server

Core T-SQL SELECT statement elements

Advanced SELECT techniques

Summary

Introducing R

Obtaining R

Your first line R of code in R

Learning the basics of the R language

Using R data structures

Summary

Getting Familiar with Python

Selecting the Python environment

Writing your first python code

Using functions, branches, and loops

Organizing the data

Integrating SQL Server and ML

Summary

Data Overview

Getting familiar with a data science project life cycle

Ways to measure data values

Introducing descriptive statistics for continuous variables

Using frequency tables to understand discrete variables

Showing associations graphically

Summary

Data Preparation

Handling missing values

Creating dummies

Discretizing continuous variables

The entropy of a discrete variable

Advanced data preparation topics

Summary

Intermediate Statistics and Graphs

Exploring associations between continuous variables

Measuring dependencies between discrete variables

Discovering associations between continuous and discrete variables

Expressing dependencies with a linear regression formula

Summary

Unsupervised Machine Learning

Installing ML services (In-Database) packages

Performing market-basket analysis

Finding clusters of similar cases

Principal components and factor analyses

Summary

Supervised Machine Learning

Evaluating predictive models

Using the Naive Bayes algorithm

Predicting with logistic regression

Trees, forests, and more trees

Predicting with T-SQL

Summary

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Supervised Machine Learning

In Chapter 6, Intermediate Statistics and Graphs, we read about intermediate statistics with linear regression. I will continue this chapter from that point. Linear regression is already an algorithm you can use for predictions. You can make predictions with the directed, or the supervised, algorithms. Supervised algorithms have a target, or a dependent variable. They try to explain the values of that variable with the formula and the values of the independent variables. This explanation is stored in a model, which you use to predict the target variable value on a new dataset. The dependent variable supervises the development of the model. In a real project, you create many models, and then you deploy the one that suits your business needs the best. Therefore, you need to evaluate the models before the deployment.

In this chapter, I will...

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