All Videos Tagged Microsoft (Data Science Central) - Data Science Central 2021-02-28T01:03:56Z https://www.datasciencecentral.com/video/video/listTagged?tag=Microsoft&rss=yes&xn_auth=no Creating Efficient Ways to Connect Across Disparate Data Sources tag:www.datasciencecentral.com,2017-03-22:6448529:Video:541718 2017-03-22T00:59:14.365Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/creating-efficient-ways-to-connect-across-disparate-data-sources"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781530876?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Many companies around the world face a number of challenges when it comes to the operations of their business. For example, transportation and logistics companies combat challenges such as moving a diversified portfolio of goods to an ever-expanding marketplace, fluctuating fuel and fleet costs, infrastructure and the constant consumer… <a href="https://www.datasciencecentral.com/video/creating-efficient-ways-to-connect-across-disparate-data-sources"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781530876?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Many companies around the world face a number of challenges when it comes to the operations of their business. For example, transportation and logistics companies combat challenges such as moving a diversified portfolio of goods to an ever-expanding marketplace, fluctuating fuel and fleet costs, infrastructure and the constant consumer expectation to transport items faster.<br /> <br /> Dealing with these challenges are important, and significantly impact the operation of a company. Data these companies collect can help address the challenges. The problem is that the data resides in silos across the organization, manual manipulation of data is time-consuming, and operational data may reside in separate systems, thus leading to complex reporting and poor visibility into what is going on in the business.<br /> <br /> Join us for this latest Data Science Central webinar and learn how Knight Transportation utilizes Alteryx and Microsoft Power BI to overcome data problems and operate more efficiently by:<br /> <br /> Removing the frustrations of traditional reporting tools<br /> Creating efficient ways to connect across disparate data sources<br /> Generating and sharing more effective insights<br /> Speakers:<br /> <br /> Treyson Marks, Director of Data Analytics -- Knight Transportation<br /> Miguel Martinez, Senior Product Manager -- Microsoft<br /> Amelia Glum, Senior Solutions Engineer -- Alteryx<br /> Dan Ganancial, 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