Featured Videos - Data Science Central 2017-09-21T21:07:27Z http://www.datasciencecentral.com/video/video/rss?xn_auth=no&featured=1 Predictive Analytics for Supply Chain Management tag:www.datasciencecentral.com,2017-08-22:6448529:Video:610229 2017-08-22T23:39:00.898Z Tim Matteson http://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="http://www.datasciencecentral.com/video/predictive-analytics-for-supply-chain-management"><br /> <img alt="Thumbnail" height="135" src="http://api.ning.com:80/files/gSdbrsPropUzUeqwI6J-4IG6Mnkmri66MAVtHDm1*spLikyYQXPCoPPXDODhsuPnQENzLJfG3JPjTVxMQN3fwXKjFc7e0QQw/1440982072.jpeg?width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The Retail and Consumer Packaged Goods industries are continuously looking to maximize margins across all aspects of their supply chain. Tight competition and increasing distribution costs can exert negative pressures on revenue, sink profits, or force companies to adopt unfavorable pricing strategies to keep pace with ever-evolving markets and consumer… <a href="http://www.datasciencecentral.com/video/predictive-analytics-for-supply-chain-management"><br /> <img src="http://api.ning.com:80/files/gSdbrsPropUzUeqwI6J-4IG6Mnkmri66MAVtHDm1*spLikyYQXPCoPPXDODhsuPnQENzLJfG3JPjTVxMQN3fwXKjFc7e0QQw/1440982072.jpeg?width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The Retail and Consumer Packaged Goods industries are continuously looking to maximize margins across all aspects of their supply chain. Tight competition and increasing distribution costs can exert negative pressures on revenue, sink profits, or force companies to adopt unfavorable pricing strategies to keep pace with ever-evolving markets and consumer demands.<br /> <br /> In this Data Science Central webinar, data experts from Alteryx and Keyrus will show how organizations can model their entire supply chain, identify cost and distribution bottlenecks, keep store shelves stocked with accurate forecasting, and model the cost-to-serve a product across dynamic and complex distribution systems.<br /> <br /> Join us to discover how putting advanced analytics within reach of your organization can lead to disruptive and actionable insights for your business.<br /> <br /> Speakers:<br /> <br /> Razvan Nistor, Head of Data Science &amp; Retail Analytics Expert -- Keyrus<br /> Scott Trauthen, Director, Product &amp; Alliances -- Alteryx<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director -- Data Science Central Parallelize R Code Using Apache® Spark™ tag:www.datasciencecentral.com,2017-08-15:6448529:Video:607234 2017-08-15T23:37:42.031Z Tim Matteson http://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="http://www.datasciencecentral.com/video/parallelize-r-code-using-apache-spark"><br /> <img alt="Thumbnail" height="135" src="http://api.ning.com:80/files/efdu9osK9OGp3MxZCVDSJlkIY-jSdiKknEHr2x1J-3agsEvnU4YPMtKw5Eu*0L1Gm7-oWM9OetXr0Uz63jiePysTlbPD50Gb/1400314612.jpeg?width=240&amp;height=135" width="240"></img><br /> </a> <br></br>R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. SparkR’s evolving interface to Apache Spark offers a wide range of APIs and capabilities to Data Scientists and Statisticians. With the release of Spark 2.0, and subsequent releases, the R API officially supports executing user code on distributed data. This is done… <a href="http://www.datasciencecentral.com/video/parallelize-r-code-using-apache-spark"><br /> <img src="http://api.ning.com:80/files/efdu9osK9OGp3MxZCVDSJlkIY-jSdiKknEHr2x1J-3agsEvnU4YPMtKw5Eu*0L1Gm7-oWM9OetXr0Uz63jiePysTlbPD50Gb/1400314612.jpeg?width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />R is the latest language added to Apache Spark, and the SparkR API is slightly different from PySpark. SparkR’s evolving interface to Apache Spark offers a wide range of APIs and capabilities to Data Scientists and Statisticians. With the release of Spark 2.0, and subsequent releases, the R API officially supports executing user code on distributed data. This is done primarily through a family of apply() functions.<br /> <br /> In this Data Science Central webinar, we will explore the following:<br /> <br /> ●Provide an overview of this new functionality in SparkR.<br /> <br /> ●Show how to use this API with some changes to regular code with dapply().<br /> <br /> ●Focus on how to correctly use this API to parallelize existing R packages.<br /> <br /> ●Consider performance and examine correctness when using the apply family of functions in SparkR.<br /> <br /> Speaker: Hossein Falaki, Software Engineer -- Databricks Inc.<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central The Art of Self-Service Data Analytics tag:www.datasciencecentral.com,2017-07-27:6448529:Video:598647 2017-07-27T20:56:12.065Z Tim Matteson http://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="http://www.datasciencecentral.com/video/the-art-of-self-service-data-analytics"><br /> <img alt="Thumbnail" height="135" src="http://api.ning.com:80/files/yuRuDW9tOZJLQo5zRq4bAKaVv254YtB9HPnISa7opfImTfi3XdcgeJkWq-OXMZqXYzYdmy7lP6ZrHG2BpI*L8V78VNUhtB9J/1308490295.jpeg?width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Regardless of the amount of resources – headcount, time, budget, etc. – your organization or department has or doesn’t have, data-driven insights are critical for growing the business, but uncovering “a-ha” moments is no easy feat. Most organizations also combine a number of business models within one operation and have numerous objectives to consider in everyday… <a href="http://www.datasciencecentral.com/video/the-art-of-self-service-data-analytics"><br /> <img src="http://api.ning.com:80/files/yuRuDW9tOZJLQo5zRq4bAKaVv254YtB9HPnISa7opfImTfi3XdcgeJkWq-OXMZqXYzYdmy7lP6ZrHG2BpI*L8V78VNUhtB9J/1308490295.jpeg?width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Regardless of the amount of resources – headcount, time, budget, etc. – your organization or department has or doesn’t have, data-driven insights are critical for growing the business, but uncovering “a-ha” moments is no easy feat. Most organizations also combine a number of business models within one operation and have numerous objectives to consider in everyday decision-making. Museums are no different. What's more, museums face significant headwinds, such as limited headcount, increasing costs and an uncertain economic environment.<br /> <br /> Join this Data Science Central Webinar and learn how the Art Institute of Chicago (ARTIC) has leveraged self-service analytics to:<br /> <br /> Grow attendance and operating revenue through more informed decision-making<br /> Strengthen operations to share more of its collection online<br /> Understand its audiences with the aid of machine learning<br /> Through specific examples of the museum's successes, failures, and efforts in-between, you'll gain relevant insight into how to introduce or scale analytics in a resource-limited environment.<br /> <br /> Speakers:<br /> <br /> Dr. Andrew Simnick, SVP Finance, Strategy and Operations -- Art Institute of Chicago<br /> Matthew Norris, Executive Director of Analytics -- Art Institute of Chicago<br /> Raman Kaler, Alliance and Product Manager -- Alteryx<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director -- Data Science Central A Language for Visual Analytics tag:www.datasciencecentral.com,2017-07-26:6448529:Video:598213 2017-07-26T08:05:43.889Z Tim Matteson http://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="http://www.datasciencecentral.com/video/a-language-for-visual-analytics"><br /> <img alt="Thumbnail" height="135" src="http://api.ning.com:80/files/PG2F6G76DOk2K7LmB3Pw-MR3jmUsAiAcaFI-wWl1XWR*W2ikxzWR2KA*wNqfdfrwzI9ifCjYdc*CSkIdyfeanEat94loOHlU/1308035611.jpeg?width=240&amp;height=135" width="240"></img><br /> </a> <br></br>In this Data Science Central webinar hear from Jock Mackinlay, VP of Research and Design at Tableau, as he describes how he used a linguistic approach inspired by the work of Jacques Bertin to influence the development of visual analysis. He will share the journey from his days at Stanford while studying for his PhD, continuing right up to present day with his current work… <a href="http://www.datasciencecentral.com/video/a-language-for-visual-analytics"><br /> <img src="http://api.ning.com:80/files/PG2F6G76DOk2K7LmB3Pw-MR3jmUsAiAcaFI-wWl1XWR*W2ikxzWR2KA*wNqfdfrwzI9ifCjYdc*CSkIdyfeanEat94loOHlU/1308035611.jpeg?width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />In this Data Science Central webinar hear from Jock Mackinlay, VP of Research and Design at Tableau, as he describes how he used a linguistic approach inspired by the work of Jacques Bertin to influence the development of visual analysis. He will share the journey from his days at Stanford while studying for his PhD, continuing right up to present day with his current work as the VP of Research and Design at Tableau Software. Jock will share his thoughts about the future of visual analysis.<br /> <br /> Speaker: Jock Mackinlay, VP of Research and Design -- Tableau<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Self-Service Machine Learning tag:www.datasciencecentral.com,2017-07-18:6448529:Video:593121 2017-07-18T22:50:36.477Z Tim Matteson http://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="http://www.datasciencecentral.com/video/self-service-machine-learning"><br /> <img alt="Thumbnail" height="135" src="http://api.ning.com:80/files/osVUbiELUl51WrFFbfdvPum4sP4jf5C2XHL*jVZc2nRP4op3HyLtaqDK**4tAzKbuMTDZerEbqCtR5ol-RRazitdFd5o0LmZ/1305919093.jpeg?width=240&amp;height=135" width="240"></img><br /> </a> <br></br>In today’s demanding market, Machine Learning capabilities have become a basic requirement you need to support. Self-service BI solutions are no different. Users need machine learning capabilities as an integral part of the provided solution.<br></br> <br></br> The specific challenges of integrating ML capabilities in a self-service BI platform include the supply of ML algorithms… <a href="http://www.datasciencecentral.com/video/self-service-machine-learning"><br /> <img src="http://api.ning.com:80/files/osVUbiELUl51WrFFbfdvPum4sP4jf5C2XHL*jVZc2nRP4op3HyLtaqDK**4tAzKbuMTDZerEbqCtR5ol-RRazitdFd5o0LmZ/1305919093.jpeg?width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />In today’s demanding market, Machine Learning capabilities have become a basic requirement you need to support. Self-service BI solutions are no different. Users need machine learning capabilities as an integral part of the provided solution.<br /> <br /> The specific challenges of integrating ML capabilities in a self-service BI platform include the supply of ML algorithms that do not rely on specific data and can be easily applied and fit to customer specific use cases (and data).<br /> <br /> In this Data Science Central Webinar we will demonstrate how to implement a classification model in a generic manner that can be used by many customers, without relying on specific data, and by automating the validation process ensuring minimum overfit introduced. It will outline the challenges in such a scenario and ways to mitigate them. Specifically, the case study will demonstrate implementing a Decision Tree model and visualize it using a dynamic UI component.<br /> <br /> Speakers:<br /> <br /> Nir Regev, Senior Data Scientist, Data Scientist Team Leader -- Sisense<br /> Evan Castle, Product Manager -- Sisense<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director -- Data Science Central