Data Science with SQL Server Quick Start Guide (Paperback or Softback)

Get unique insights from your data by combining the power of SQL Server, R and Python

Key Features

• Use the features of SQL Server 2017 to implement the data science project life cycle
• Leverage the power of R and Python to design and develop efficient data models
• find unique insights from your data with powerful techniques for data preprocessing and analysis

Book Description

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.

What you will learn

• Use the popular programming languages,T-SQL, R, and Python, for data science
• Understand your data with queries and introductory statistics
• Create and enhance the datasets for ML
• Visualize and analyze data using basic and advanced graphs
• Explore ML using unsupervised and supervised models
• Deploy models in SQL Server and perform predictions

Who this book is for

SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.

Table of Contents

1. Writing Queries with T-SQL
2. Introducing R
3. Getting Familiar with Python
4. Data Overview
5. Data Preparation
6. Intermediate Statistics and Graphs
7. Unsupervised Machine Learning
8. Supervised Machine Learning