All Videos Tagged Azure (Data Science Central) - Data Science Central 2020-03-29T10:01:13Z https://www.datasciencecentral.com/video/video/listTagged?tag=Azure&rss=yes&xn_auth=no DSC Webinar Series: Embedded Analytics & Data Science: Shell Oil Case Study tag:www.datasciencecentral.com,2018-09-14:6448529:Video:760539 2018-09-14T18:43:24.799Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-embedded-analytics-data-science-shell-oil-case"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781532615?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Shell has become a world leader in analytics, driving huge gains in safety and maintenance that save lives and resources. With a limited number of data scientists in the world, Shell realized that in order to maintain and scale their analytics success, they needed to expand the capabilities of business analysts.<br></br> <br></br>  …<br></br> <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-embedded-analytics-data-science-shell-oil-case"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781532615?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Shell has become a world leader in analytics, driving huge gains in safety and maintenance that save lives and resources. With a limited number of data scientists in the world, Shell realized that in order to maintain and scale their analytics success, they needed to expand the capabilities of business analysts.<br /> <br />  <br /> <br /> In this latest Data Science Central webinar, see how Shell uses Alteryx + Azure Databricks to address the biggest barriers to their success. Learn how to: <br /> <br /> Design an analytic stack that leverages self-service analytics and allows users to do data science at the massive scale they require<br /> Bridge the skills gap data analysts have and empower them to setup, configure, and maintain advanced analytics engines such as Apache Spark <br /> Create a trust-based analytic culture where data scientists can craft reusable models that allow analysts to repeatedly run them with new datasets<br /> Learn why Shell brought these two platforms together and how the collaboration improves the insight process.  Then see Alteryx and Azure Databricks in action together to understand how your organization can replicate such success as well.<br /> <br /> <br /> <br /> Speakers:<br /> Chris Bridge, Sr. Data Engineer -- Shell Oil<br /> Brian Dirking, Sr. Director -- Databricks<br /> Nauman Fakhar, Director of ISV Solutions -- Databricks<br /> Raman Kaler, Product &amp; Alliance Manager -- Alteryx<br /> <br /> Hosted by: <br /> Bill Vorhies, Editorial Director -- Data Science Central Get To Business Insights Quicker With In-Database Blending tag:www.datasciencecentral.com,2016-05-11:6448529:Video:422722 2016-05-11T21:14:29.847Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/get-to-business-insights-quicker-with-in-database-blending"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781531188?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Analysts in Sales, Marketing, Finance and Operations have to prepare and analyze data quickly to answer important business questions. Time spent blending data takes away from time spent analyzing and acting on it. Analysts need an easier way to work with data in a database in order to quickly go from data blending to producing insights.… <a href="https://www.datasciencecentral.com/video/get-to-business-insights-quicker-with-in-database-blending"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781531188?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Analysts in Sales, Marketing, Finance and Operations have to prepare and analyze data quickly to answer important business questions. Time spent blending data takes away from time spent analyzing and acting on it. Analysts need an easier way to work with data in a database in order to quickly go from data blending to producing insights.<br /> <br /> In this latest Data Science Central webinar we will show you how to:<br /> <br /> Prepare your data in 1/10th of the time, and automate the process for ongoing updates<br /> Utilize a drag-and-drop interface that does not require SQL coding to prepare, blend, and analyze data<br /> Push the processing steps into the database rather than pulling the entire dataset to the processing location<br /> See how you can leverage the power of in-database blending to get to business insights quicker.<br /> <br /> Speakers:<br /> Gene Rinas, Senior Solutions Engineer, -- Alteryx<br /> Dan Ganancial, Alliance Manager -- Alteryx<br /> Ron Ortloff, Senior Program Manager -- Microsoft<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director -- Data Science Central DSC Webinar Series: Learn How To Work with Large Datasets to Build Predictive Models with Microsoft’s Analytics Toolkit tag:www.datasciencecentral.com,2015-04-01:6448529:Video:263478 2015-04-01T15:17:41.685Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-learn-how-to-work-with-large-datasets"><br /> <img alt="Thumbnail" height="180" src="https://storage.ning.com/topology/rest/1.0/file/get/2781529249?profile=original&amp;width=240&amp;height=180" width="240"></img><br /> </a> <br></br>In today’s webinar we will use a case study of NY taxi data to discuss and cover how:<br></br> • Azure provides the infrastructure for storing and manipulating large data sets<br></br> • Azure ML provides an algorithm (Learning with Counts) to train a predictive model with large data sets<br></br> • To created a model to predict tips on NY Taxi rides using… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-learn-how-to-work-with-large-datasets"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781529249?profile=original&amp;width=240&amp;height=180" width="240" height="180" alt="Thumbnail" /><br /> </a><br />In today’s webinar we will use a case study of NY taxi data to discuss and cover how:<br /> • Azure provides the infrastructure for storing and manipulating large data sets<br /> • Azure ML provides an algorithm (Learning with Counts) to train a predictive model with large data sets<br /> • To created a model to predict tips on NY Taxi rides using Azure storage, HDInsight and Azure ML