
Data Science with SQL Server Quick Start Guide
Description
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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 DescriptionSQL 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 forSQL 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.
More details
Other editions
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Person
Dejan Sarka, MCT and Microsoft Data Platform MVP, is an independent trainer and consultant who focuses on the development of database and business intelligence applications. He is the founder of the Slovenian SQL Server and .NET Users Group.
Content
Writing Queries with T-SQL
Introducing R
Getting Familiar with Python
Data Overview
Data Preparation
Intermediate Statistics and Graphs
Unsupervised Machine Learning
Supervised Machine Learning
System requirements
File format: ePUB
Copy protection: Adobe-DRM (Digital Rights Management)
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