Data Science with Microsoft SQL Server 2016
This book covers the combined environments of RDBMS and the R language inside Microsoft SQL Server 2016. Readers can learn to use this new capability, and practical, hands-on examples of using SQL Server R Services to create real-world solutions.
Tag(s): Data Science Relational Database
Publication date: 01 Oct 2016
ISBN-10: n/a
ISBN-13: 9781509304318
Paperback: 90 pages
Views: 9,964
Data Science with Microsoft SQL Server 2016
This book covers the combined environments of RDBMS and the R language inside Microsoft SQL Server 2016. Readers can learn to use this new capability, and practical, hands-on examples of using SQL Server R Services to create real-world solutions.
Publication date: 01 Oct 2016
ISBN-10: n/a
ISBN-13: 9781509304318
Paperback: 90 pages
Views: 9,964
Document Type: Book
Publisher: Microsoft Press Books
License: Open Access
Post time: 25 Oct 2016 05:00:00
R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Actionable business data is often stored in Relational Database Management Systems (RDBMS), and one of the most widely used RDBMS is Microsoft SQL Server. Much more than a database server, it's a rich ecostructure with advanced analytic capabilities. Microsoft SQL Server R Services combines these environments, allowing direct interaction between the data on the RDBMS and the R language, all while preserving the security and safety the RDBMS contains. In this book, you'll learn how Microsoft has combined these two environments, how a data scientist can use this new capability, and practical, hands-on examples of using SQL Server R Services to create real-world solutions.
How this book is organized
This book breaks down into three primary sections: an introduction to the SQL Server R Services and SQL Server in general, a description and explanation of how a data scientist works in this new environment (useful, given that many data scientists work in "silos," and this new way of working brings them in to the business development process), and practical, hands-on examples of working through real-world solutions. The reader can either review the examples, or work through them with the chapters.
Who this book is for
The intended audience for this book is technical—specifically, the data scientist—and is assumed to be familiar with the R language and environment. We do, however, introduce data science and the R language briefly, with many resources for the reader to go learn those disciplines, as well, which puts this book within the reach of database administrators, developers, and other data professionals. Although we do not cover the totality of SQL Server in this book, references are provided and some concepts are explained in case you are not familiar with SQL Server, as is often the case with data scientists.
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About The Author(s)
Gagan Bansal is a data scientist leading the development of financial forecasting capabilities in Cortana Analytics at Microsoft. Gagan joined Microsoft from Yahoo Labs, where he was a lead engineer building and deploying large-scale user modeling and scoring pipelines on both grid (Hadoop) and stream scoring systems for display-ad targeting applications.
Gagan Bansal is a data scientist leading the development of financial forecasting capabilities in Cortana Analytics at Microsoft. Gagan joined Microsoft from Yahoo Labs, where he was a lead engineer building and deploying large-scale user modeling and scoring pipelines on both grid (Hadoop) and stream scoring systems for display-ad targeting applications.
Matt Conners is a senior data sciences program manager in Microsoft’s Algorithms and Data Sciences group. He is focused on the forecasting domain, working with customers, partners, and data scientists to operationalize machine learning financial forecasting solutions. He has extensive business operations and industry domain experience, with more than 20 years’ of financial technology experience across sales, marketing, business operations, securities, and banking.
Matt Conners is a senior data sciences program manager in Microsoft’s Algorithms and Data Sciences group. He is focused on the forecasting domain, working with customers, partners, and data scientists to operationalize machine learning financial forecasting solutions. He has extensive business operations and industry domain experience, with more than 20 years’ of financial technology experience across sales, marketing, business operations, securities, and banking.
Danielle Dean is a senior data scientist lead at Microsoft in the Algorithms and Data Science group. She leads a team of data scientists and engineers on end- to-end analytics projects that use Microsoft's Cortana Intelligence Suite for applications ranging from automating the ingestion of data to analyzing and implementing algorithms, creating web services of these implementations, and integrating them into customer solutions or building end-user dashboards and visualizations.
Danielle Dean is a senior data scientist lead at Microsoft in the Algorithms and Data Science group. She leads a team of data scientists and engineers on end- to-end analytics projects that use Microsoft's Cortana Intelligence Suite for applications ranging from automating the ingestion of data to analyzing and implementing algorithms, creating web services of these implementations, and integrating them into customer solutions or building end-user dashboards and visualizations.
Debraj GuhaThakurta is a senior data Scientist at Microsoft in the Algorithms and Data Science group. His effort focuses on the use of different platforms and toolkits such as Microsoft’s Cortana Intelligence suite, Microsoft R Server, SQL Server, Hadoop, and Spark for creating scalable and operationalized analytical processes for business problems. He has a Ph.D. in chemistry and biophysics, and post-doctoral research experience in machine learning applications in bio-informatics.
Debraj GuhaThakurta is a senior data Scientist at Microsoft in the Algorithms and Data Science group. His effort focuses on the use of different platforms and toolkits such as Microsoft’s Cortana Intelligence suite, Microsoft R Server, SQL Server, Hadoop, and Spark for creating scalable and operationalized analytical processes for business problems. He has a Ph.D. in chemistry and biophysics, and post-doctoral research experience in machine learning applications in bio-informatics.
Wee-Hyong Tok is a senior data scientist lead at Microsoft in the Algorithms and Data Science group. Wee-Hyong has decades of database systems experience, spanning academia and industry, including deep experience driving and shipping products and services that include distributed engineering teams from Asia and the United States.
Wee-Hyong Tok is a senior data scientist lead at Microsoft in the Algorithms and Data Science group. Wee-Hyong has decades of database systems experience, spanning academia and industry, including deep experience driving and shipping products and services that include distributed engineering teams from Asia and the United States.
Buck Woody works on the Microsoft Machine Learning and Data Science Team, using data and technology to educate others on solving business and science problems. With more than 30 years of professional and practical experience in computer data technologies, he is also a popular speaker at many conferences around the world. He teaches database courses and sits on the Data Science Board at the University of Washington, and specializes in data analysis techniques.
Buck Woody works on the Microsoft Machine Learning and Data Science Team, using data and technology to educate others on solving business and science problems. With more than 30 years of professional and practical experience in computer data technologies, he is also a popular speaker at many conferences around the world. He teaches database courses and sits on the Data Science Board at the University of Washington, and specializes in data analysis techniques.