Data Science with SQL Server Quick Start Guide by Sarka, Dejan -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Data Science with SQL Server Quick Start Guide

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Contributors

About the author About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Writing Queries with T-SQL

Before starting – installing SQL Server

SQL Server setup 

Core T-SQL SELECT statement elements

The simplest form of the SELECT statement Joining multiple tables Grouping and aggregating data

Advanced SELECT techniques

Introducing subqueries Window functions Common table expressions Finding top n rows and using the APPLY operator

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

Calculating centers of a distribution Measuring the spread Higher population moments

Using frequency tables to understand discrete variables Showing associations graphically Summary

Data Preparation

Handling missing values Creating dummies Discretizing continuous variables

Equal width discretization Equal height discretization Custom discretization

The entropy of a discrete variable Advanced data preparation topics

Efficient grouping and aggregating in T-SQL Leveraging Microsoft scalable libraries in Python Using the dplyr package in R

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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