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DESCRIPTION:Click for Latest Location Information: http://edw2020.dataversi
 ty.net/sessionPop.cfm?confid=128&proposalid=11364\n<p dir="ltr">With radica
 lly cheaper storage and an exponential increase in data volumes available f
 or analysis, modern data teams are finding answers to questions that were r
 ecently considered too daunting to even approach. As a result of companies 
 becoming more mature in their usage of data, they&rsquo;re finding that tra
 ditional tools and processes are no longer adequate to serve as one-size-fi
 ts-all for data analysis. Advanced data questions require advanced data mod
 els and languages to transform and visualize information in a way that unco
 vers new insights.&nbsp;</p>\n<p dir="ltr">SQL was often enough to handle a
 ll of a company&rsquo;s data requests, but today&rsquo;s advanced transform
 ations and visualizations require the use of languages like R and Python to
  perform deeper analyses. By using SQL for the heavy data lifting and Pytho
 n/R for the polishing, the data workflow is evolving to give more mature da
 ta companies a serious competitive advantage. Furthermore, rather than havi
 ng a team of specialists for each language, mature data teams are getting t
 he most out of a dataset with a team of generalists who have skills with mu
 ltiple languages.</p>\n<p>In this presentation, Scott Castle&nbsp;will disc
 uss the advantages of implementing SQL vs. Python and&nbsp;R as part of you
 r strategy, and how to bring the workflows of all three together on one pla
 tform. To illustrate how SQL and Python work together to answer complex dat
 a questions, Scott will conclude with a live coding demo on stage.&nbsp;</p
 >\n<p>Attendees will learn:&nbsp;</p>\n\n	\n
 <p>Why traditional data analysis tools are no longer adequate for big data&
 nbsp;</p>\n	\n	\n
 <p>How developing and bringing the workflows of Python, R, and SQL together
  on a single platform will enhance progress and results within your organiz
 ation</p>\n	\n	\n
 <p>How business intelligence has evolved and what tools and capabilities wi
 ll make organizations more competitive&nbsp;&nbsp;</p>\n	\n	\n
 <p>An overview of traditional data tools vs. the tools and methods of today
  for data analysis</p>\n	\n
DTSTART:20200326T094500
SUMMARY:Answering Deeper Data Questions with SQL, Python, and R Together
DTEND:20200326T104459
LOCATION: See Description
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