Book Image

Data Science with SQL Server Quick Start Guide

By : Dejan Sarka
Book Image

Data Science with SQL Server Quick Start Guide

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.

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Table of Contents (15 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
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
2
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
3
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
4
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
5
Data Preparation
Data Preparation
Handling missing values
Creating dummies
Discretizing continuous variables
The entropy of a discrete variable
Advanced data preparation topics
Summary
6
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
7
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
8
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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Index
Index

Summary

So, in this chapter, you started with some real work. You were given a brief introduction into statistical measures that describe the distribution of a variable, and how to calculate them in three different languages. This visualization of statistics and data can sometimes already be the final result, and everything you need for a final report. However, in a data science project, this is just an initial step.

In a real-life project, you spend about 70% of the time with data overview and data preparation. This means that there is some more tedious work waiting for you in the next chapter, where I will deal more with the data preparation tasks.

Previous Section
End of Chapter 4
Next Chapter