Applied Data Science for the SQL Server Professional [BI]

Speaker: Ginger Grant

Duration: Full Day

Track: Analyze

This full day session is designed to be a practical revelation of Data Science concepts tailored for data professionals.  Attendees will learn the processes data scientist employ in problem solving to understand how they can be implemented in a data environment. 
The methods used to create a data science solution are broken down into understandable sections, to make the concepts readily grasped and provide a structure to implement them in real-world environments. By first understanding the process, then building on that by learning methods for algorithm selection, solutions can be created and applied using Python, Azure ML, R or Power BI.  As these tools and languages are not well known in the data community, each will be introduced to provide a basic understanding of each, as no prior data science knowledge is expected by session attendees.

Deploying data science solutions is a topic nearly as important as creating the solution itself as improperly deployed the solution cannot be successful.  This course will also cover how to optimally configure a SQL Server 2016 or 2017 environment to run and monitor data science solutions in R and Python on SQL Server without adversely impacting other SQL Server operations.  Learn how R and Python solutions can be deployed and what can be done to improve the solution performance. Visualization techniques in the solution will be explored so that attendees will know their options and the best tool for the job, which may be Power BI or SQL Server itself.

With the introduction of SQL Server 2017, there are an increasing number of different tools, including, R, Python, Azure ML, and Power BI, which can be used to implement data science solutions. This session explores how to determine the best tool for a given situation by revealing the strengths and weakness of each technology to provide attendees with the knowledge they will need for that evaluation in their unique environment. 


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