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Data Science with SQL Server Quick Start Guide

By : Dejan Sarka

4 (1)

Data Science with SQL Server Quick Start Guide

4 (1)

By: Dejan Sarka

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

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

Writing Queries with T-SQL

Writing Queries with T-SQL

Before starting – installing SQL Server

Core T-SQL SELECT statement elements

Advanced SELECT techniques

Summary

Introducing R

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

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

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

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

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

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

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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Getting familiar with a data science project life cycle

A long-term data science project is somehow never finished. It has its own complete life cycle. This virtuous cycle includes the following steps:

Identify the business problem
Use data mining and machine learning techniques to Transform the data into actionable information
Act on the information
Measure the result

Data science is not a product. Data science gives you a platform for continuous learning on how to improve your business. In order to learn how to exploit data mining maximally, you need to measure the results of your actions based on the information extracted with data mining. Measurement provides the feedback for continuously improving results. You can see the life cycle in the following diagram:

Figure 4.1: Data science project life cycle

Let me give you an example. For credit card issuers and online banking, fraud detection is quite a common task. You want to identify fraudulent transactions as quickly as possible to minimize...

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