All Videos Tagged Data (Data Science Central) - Data Science Central 2020-01-23T03:19:19Z https://www.datasciencecentral.com/video/video/listTagged?tag=Data&rss=yes&xn_auth=no DSC Webinar Series: Weaponizing Data in the Fight Against White Collar Crimes tag:www.datasciencecentral.com,2020-01-21:6448529:Video:924729 2020-01-21T22:04:03.201Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-weaponizing-data-in-the-fight-against-white"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3830023345?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>In this webinar analyst and advisor Alasdair Anderson will discuss how escalating global white collar crime activity is driving adoption of advanced analytics at a pace never before seen in mature enterprises. Organizations are having to change the way they deal with data and change the types of data that they utilize. The old data… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-weaponizing-data-in-the-fight-against-white"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3830023345?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />In this webinar analyst and advisor Alasdair Anderson will discuss how escalating global white collar crime activity is driving adoption of advanced analytics at a pace never before seen in mature enterprises. Organizations are having to change the way they deal with data and change the types of data that they utilize. The old data management adage of "garbage in, garbage out" has been left behind as organizations realize that agile wrangling of all data is far more valuable that static historic curated data sets.<br /> <br /> In this latest Data Science Central webinar you will learn:<br /> <br /> How large organizations are using data to fight white collar crimes<br /> The crucial role cloud technology plays in advanced analytics<br /> Why data wrangling is a key functionality in dealing with "garbage" data<br /> <br /> Speakers:<br /> Alasdair Anderson, Independent Consultant<br /> Matt Derda, Customer Marketing Manager - Trifacta<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: ML vs Holt-Winters Forecasting tag:www.datasciencecentral.com,2020-01-15:6448529:Video:922800 2020-01-15T01:25:37.759Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-vs-holt-winters-forecasting-1"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3819607266?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare?<br></br> <br></br> In this latest Data Science Central webinar, we will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-vs-holt-winters-forecasting-1"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3819607266?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Machine Learning is all the rage, but when does it make sense to use it for forecasting? How do statistical forecasting methods compare?<br /> <br /> In this latest Data Science Central webinar, we will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.<br /> <br /> <br /> Speaker:<br /> Anais Dotis-Georgiou, Developer Advocate - InfluxData<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: From Degas to Dashboards: Lessons of the Great Masters tag:www.datasciencecentral.com,2019-12-17:6448529:Video:915663 2019-12-17T23:53:02.128Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-from-degas-to-dashboards-lessons-of-the-great"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3774995441?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>For over 30,000 years, we have expressed ourselves through visual art, and there are lessons we can draw from painting and apply them to viz. What do Impressionists teach us about dashboard interactivity? How does Cubism help us tell a data story? Set against a canvas of art history, in this latest Data Science Central webinar we will… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-from-degas-to-dashboards-lessons-of-the-great"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3774995441?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />For over 30,000 years, we have expressed ourselves through visual art, and there are lessons we can draw from painting and apply them to viz. What do Impressionists teach us about dashboard interactivity? How does Cubism help us tell a data story? Set against a canvas of art history, in this latest Data Science Central webinar we will learn a dozen specific techniques and tools for building meaningful, engaging, and visually striking dashboards.<br /> <br /> Speaker:<br /> Jeff Pettiross, User Experience Designer - Tableau<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: ML/AI Models: Continuous Integration & Deployment tag:www.datasciencecentral.com,2019-12-11:6448529:Video:914196 2019-12-11T23:49:08.649Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-ai-models-continuous-integration-deployme-1"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3765904026?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Some things are best learned through real-world experience. Machine learning is no different. Getting machine learning right requires evolving your analytics platform to support moving data science from research into operations. It all begins with repeatable data wrangling processes that support building and deploying models. It also… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-ml-ai-models-continuous-integration-deployme-1"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3765904026?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Some things are best learned through real-world experience. Machine learning is no different. Getting machine learning right requires evolving your analytics platform to support moving data science from research into operations. It all begins with repeatable data wrangling processes that support building and deploying models. It also requires collaboration between data scientists, engineers and business analysts. With the help of tools like SAS® Model Manager, these teams can continuously and automatically train models at scale and ensure the best models are put into production.<br /> <br /> In this latest Data Science Central webinar we will discuss:<br /> <br /> Model validation best practices<br /> Various model deployment options including open source models<br /> Model scoring and training services<br /> Model performance monitoring<br /> Orchestrating a continuous learning platform<br /> <br /> <br /> Speakers:<br /> Wayne Thompson, Chief Data Scientist - SAS<br /> Lora Edwards, Principal Product Manager - SAS<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: How Bank of America Uses Data Prep for Faster Reporting tag:www.datasciencecentral.com,2019-12-11:6448529:Video:913900 2019-12-11T00:11:20.871Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-bank-of-america-uses-data-prep-for-faster"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3764395366?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Knowledge workers typically a) get information b) perform logic on that information and c) finally reach a conclusion and can take action. The time to just clean and prepare data for analysis can cause significant bottlenecks and delay the ability to take any action. Automating these series of tasks, mechanizes repetitive and manual tasks.… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-bank-of-america-uses-data-prep-for-faster"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3764395366?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Knowledge workers typically a) get information b) perform logic on that information and c) finally reach a conclusion and can take action. The time to just clean and prepare data for analysis can cause significant bottlenecks and delay the ability to take any action. Automating these series of tasks, mechanizes repetitive and manual tasks. This frees up knowledge workers to focus on more value-added activities. A series of lessons will be shared with a case study from the banking trenches on how to leverage data wrangling to help automate these series of tasks on a use case within Risk Management and reduce a 10,000 hour regulatory process down to 10 hours.<br /> <br /> In this latest Data Science webinar you will learn:<br /> <br /> How manual and repetitive tasks are costing organizations trillions of dollars in non-productive work<br /> Understand how to drive adoption of new technologies within your organization<br /> How assembly line thinking has led to mistakes in the way we approach data pipelines<br /> <br /> Speakers:<br /> Salah Khawaja, Managing Director,<br /> Automation Global Risk - Bank of America<br /> Raj Anand, Sr. Vice President,<br /> Automation Global Risk - Bank of America<br /> Will Davis, Head of Marketing - Trifacta<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central