All Videos Tagged Science (Data Science Central) - Data Science Central 2020-06-04T09:03:12Z https://www.datasciencecentral.com/video/video/listTagged?tag=Science&rss=yes&xn_auth=no DSC Webinar Series: Collaborative Data Science at Pfizer tag:www.datasciencecentral.com,2020-06-02:6448529:Video:955707 2020-06-02T21:41:41.899Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-collaborative-data-science-at-pfizer"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/5526287273?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The value of data science multiplies when it is used and applied across the organization. Successful data science should impact the business — and that requires data scientists to not only collaborate with one another but also with data consumers of differing technical expertise and experience. <br></br> <br></br> In this latest Data Science Central… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-collaborative-data-science-at-pfizer"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/5526287273?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The value of data science multiplies when it is used and applied across the organization. Successful data science should impact the business — and that requires data scientists to not only collaborate with one another but also with data consumers of differing technical expertise and experience. <br /> <br /> In this latest Data Science Central webinar, learn how Pfizer’s data science team is implementing collaboration in the right context — from processes to team structure and tools, like the Alation Data Catalog and Dataiku —  to help make a greater impact with data science. And, see how the Alation Data Catalog and Dataiku work together for seamless data science collaboration.<br /> <br /> Speaker: <br /> Neil Patel, Director of Data Science - Pfizer<br /> <br /> <br /> Hosted by: <br /> Sean Welch, Host and Producer - Data Science Central DSC Webinar Series: Embracing Responsible AI from Pilot to Production tag:www.datasciencecentral.com,2020-05-27:6448529:Video:954557 2020-05-27T22:52:42.097Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-embracing-responsible-ai-from-pilot-to"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/5403207855?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization. In this latest Data… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-embracing-responsible-ai-from-pilot-to"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/5403207855?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization. In this latest Data Science Central webinar, we’ll talk about the framework for scaling AI pilots to production with a focus on ethical responsibilities and bias mitigation at each step.<br /> <br /> We’ll look at:<br /> <br /> The five-step AI development cycle<br /> Ways to control for unwanted bias across data, models, and run time at the production layer<br /> Explainability and why it is key for moving AI pilots to production that delivers core business value<br /> <br /> Speakers: <br /> Lukas Biewald, Founder &amp; CEO - Weights &amp; Biases<br /> Alyssa Simpson Rochwerger, VP of AI &amp; Data - Appen<br /> <br /> Hosted by: <br /> Sean Welch, Host and Producer - Data Science Central DSC Webinar Series: How to Create Mathematical Optimization Models with Python tag:www.datasciencecentral.com,2020-04-29:6448529:Video:948616 2020-04-29T23:08:58.350Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-create-mathematical-optimization-models"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/4566482083?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-create-mathematical-optimization-models"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/4566482083?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain mathematical optimization applications.<br /> <br /> The Gurobi Python API borrows ideas from modeling languages, enabling users to deploy and solve mathematical optimization models with scripts that are easy to write, read, and maintain. Such modules can even be embedded in decision support systems for production-ready applications.<br /> <br /> In this latest Data Science Central webinar, we will:<br /> <br /> Discuss the motivation for using Python in mathematical optimization applications<br /> <br /> Help you understand the importance of parameterizing a mathematical optimization model<br /> Review some of the best practices for deploying mathematical optimization models in Python<br /> <br /> Speaker: <br /> Juan Orozco Guzman, Optimization Support Engineer- Gurobi Optimization<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Webinar Series: Edge Computing with Real-time Analytics at Scale tag:www.datasciencecentral.com,2019-12-12:6448529:Video:914389 2019-12-12T23:46:52.594Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-edge-computing-with-real-time-analytics-at"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3767434029?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Performing analytics at the edge is needed in today’s distributed landscape. Edge Computing allows the flexibility of virtualized computation, network and storage resources to the edge, as an integrated solution combined with ML and AI libraries. At the heart of the solution is the open-source time series database, InfluxDB, and the data… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-edge-computing-with-real-time-analytics-at"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3767434029?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Performing analytics at the edge is needed in today’s distributed landscape. Edge Computing allows the flexibility of virtualized computation, network and storage resources to the edge, as an integrated solution combined with ML and AI libraries. At the heart of the solution is the open-source time series database, InfluxDB, and the data processing framework Kapacitor.<br /> <br /> In this latest Data Science Central webinar, we will share how to build this point-and-click solution to help customers unlock the power of high-frequency data in real-time to become a data-driven organization.<br /> <br /> Speakers:<br /> Anil Joshi, CEO - AnalyticsPlus, Inc.<br /> Pankaj Bhagra, Co-Founder and Software Architect - Nebbiolo Technologies<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: Data Mastering at Scale tag:www.datasciencecentral.com,2019-10-30:6448529:Video:903806 2019-10-30T00:48:10.189Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Master data management (MDM) software turned 15 years old this year.<br></br> <br></br> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Master data management (MDM) software turned 15 years old this year.<br /> <br /> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems.<br /> <br /> MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail.<br /> <br /> Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review:<br /> <br /> Why large enterprises need data management solutions that solve data mastering challenges at scale<br /> Why traditional, rule-based, data mastering options are struggling to keep up<br /> How Machine Learning can be used to address large-scale data mastering challenges<br /> <br /> Speaker:<br /> Mike Stonebraker, CTO &amp; Co-Founder - Tamr, Inc.<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central