SQL Saturday CLE Precon: Data Analytics with SQL Server 2016, R, & Power BI

Ohio North SQL Server User Group

Advanced data analytics does not live only in the realm of data scientists.  The necessary tools now exist in SQL Server 2016 to perform advanced analytical analysis with R. This session aims to demystify data science by showing how to create advanced analytical functions and display the results. These techniques can be applied to perform in-depth analysis on practical things like improving database monitoring.  The ability to create predictive models for server performance load, determining when disk space will be required, and impact analysis of performance tuning tasks are some of the examples which will be covered in this session. Session Goals:
<UL>
<LI>Learn how to code applications in R to provide data insight and data visualizations for use within SQL Server.</LI>
<LI>Develop and understanding of some of the algorithms used in data science and how to apply them.</LI>
<LI>Extend the functionality of SQL Server by integrating R code to provide insight into the performance of SQL Server.</LI>
<LI>Understand different ways to visualize the results, including storing within SQL Server, creating SSRS reports and visualizing in Power BI.</LI>
<LI>Introduction to Data Science concepts.</LI>
<LI>Application of Data Science algorithms in R.</LI>
<LI>SQL Server R Internals and Integration.</LI>
<LI>Modifying SQL Server to optimally perform and monitor R Code.</LI>
<LI>In depth walkthrough of SQL Server running R</LI>
<LI>Introduction to Linear Regression.</LI>
<LI>Application of linear regression to determine storage space usage over time within SQL Server.</LI>
<LI>Predictive and Prescriptive Analysis techniques.</LI>
<LI>Application of predictive and prescriptive analysis through analysis of DMVs.</LI>
<LI>Common visualization techniques used in data science.</LI>
<LI>Demonstration of how visualizations are used in evaluating data algorithms.</LI>
<LI>Configuration and methods for displaying visualizations within SQL Server, providing reports in SSRS and Power BI.</LI>
</UL>
 Session Agenda:
<UL>
<LI>Introduction to Data Science concepts.</LI>
<LI>Application of Data Science algorithms in R.</LI>
<LI>SQL Server R Internals and Integration.</LI>
<LI>Modifying SQL Server to optimally perform and monitor R Code.</LI>
<LI>In depth walkthrough of SQL Server running R</LI>
<LI>Introduction to Linear Regression.</LI>
<LI>Application of linear regression to determine storage space usage over time within SQL Server.</LI>
<LI>Predictive and Prescriptive Analysis techniques.</LI>
<LI>Application of predictive and prescriptive analysis through analysis of DMVs.</LI>
<LI>Common visualization techniques used in data science.</LI>
<LI>Demonstration of how visualizations are used in evaluating data algorithms.</LI>
<LI>Configuration and methods for displaying visualizations within SQL Server, providing reports in SSRS and Power BI.</LI>
</UL>
 Presenter - Ginger Grant: Ginger Grant started working in business intelligence for a number of industries including transportation, insurance, and healthcare. Looking for answers in data led to advanced analytics and data science, where she currently creates systems to provide solutions to data questions. An active member of the data community, she is a Microsoft VTSP and MVP in Data Platform and regularly blogs at DesertIsleSQL.com. A Note from the Organizer:
This event is affiliated with SQL Saturday Cleveland 2017, which is also held at Hyland Software on 2/4/17. Please visit our event site at <A HREF="http://www.sqlsaturday.com/595/EventHome.aspx" TARGET="_blank" TITLE="http://www.sqlsaturday.com/595/EventHome.aspx" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://www.sqlsaturday.com/595/EventHome.aspx</A> for more information or to register to attend. You do not need to attend SQL Saturday to be able to attend this precon event.

Hyland Software, Inc. - Building 3

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SQL Saturday CLE Precon: Data Analytics with SQL Server 2016, R, & Power BI

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SQL Saturday CLE Precon: Data Analytics with SQL Server 2016, R, & Power BI

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