All Videos Tagged Pivotal (Data Science Central) - Data Science Central 2019-10-20T13:42:55Z https://www.datasciencecentral.com/video/video/listTagged?tag=Pivotal&rss=yes&xn_auth=no A Data Scientist’s Guide to Modeling Engine Degradation tag:www.datasciencecentral.com,2016-03-23:6448529:Video:404011 2016-03-23T00:30:50.595Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/a-data-scientists-guide-to-modeling-engine-degradation"><br /> <img alt="Thumbnail" height="180" src="https://storage.ning.com/topology/rest/1.0/file/get/2781532640?profile=original&amp;width=240&amp;height=180" width="240"></img><br /> </a> <br></br>With the growth of connected “things”, industries are presented with huge opportunities to leverage sensor data to improve their operations, products and services. With the proliferation of these devices, competitive advantages will develop from appropriate leveraging of the deluge of data. From connected appliances to jet engines, industries are… <a href="https://www.datasciencecentral.com/video/a-data-scientists-guide-to-modeling-engine-degradation"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781532640?profile=original&amp;width=240&amp;height=180" width="240" height="180" alt="Thumbnail" /><br /> </a><br />With the growth of connected “things”, industries are presented with huge opportunities to leverage sensor data to improve their operations, products and services. With the proliferation of these devices, competitive advantages will develop from appropriate leveraging of the deluge of data. From connected appliances to jet engines, industries are already undergoing massive transformations. Critical to success is the ability to not only collect data from sensors, but to also leverage big data technologies and data science expertise to extract actionable insights from the data.<br /> <br /> It is critical to be able to model degradation of a machine to prevent catastrophic events and adjust maintenance scheduling. This is true in industries including oil and gas, transportation and even consumer products.<br /> <br /> In this latest DSC webinar, the Pivotal Data Science team will present a data-driven approach to detecting and tracking jet-engine degradation using simulated sensor data. In particular we will focus on (1) data integration and cleansing, (2) transformation of time series data from sensors into meaningful features for modeling and (3) the algorithms used to build models to identify engine degradation patterns.<br /> <br /> Speakers:<br /> Sarah Aerni, Principal Data Scientist -- Pivotal<br /> April Song, Principal Data Scientist -- Pivotal<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central The Past Present and Future of Data Science A Live Roundtable tag:www.datasciencecentral.com,2016-01-12:6448529:Video:372851 2016-01-12T21:01:11.792Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/the-past-present-and-future-of-data-science-a-live-roundtable"><br /> <img alt="Thumbnail" height="180" src="https://storage.ning.com/topology/rest/1.0/file/get/2781530561?profile=original&amp;width=240&amp;height=180" width="240"></img><br /> </a> <br></br>This latest Data Science Central Webinar was recorded from a live roundtable discussion with the introduced members of the Pivotal Data Science team. This is a truly unique event that is intended to provide the DSC Community members a full hour to interact with and learn from their years of real-world experience. You, the audience, are again… <a href="https://www.datasciencecentral.com/video/the-past-present-and-future-of-data-science-a-live-roundtable"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781530561?profile=original&amp;width=240&amp;height=180" width="240" height="180" alt="Thumbnail" /><br /> </a><br />This latest Data Science Central Webinar was recorded from a live roundtable discussion with the introduced members of the Pivotal Data Science team. This is a truly unique event that is intended to provide the DSC Community members a full hour to interact with and learn from their years of real-world experience. You, the audience, are again encourage to participate and take advantage of the unique access and knowledge base provided here today.<br /> Topics discussed will include (but not be limited to):<br /> <br /> -How did you become a data scientist?<br /> -What steps did you take?<br /> -What are the most important qualities of a data scientist?<br /> -How would you describe a typical work day?<br /> -What is the largest data set you have worked on?<br /> -Which tools/platforms do you use (R, Python, Hadoop etc.)?<br /> -How do you think the field of data science will evolve in the years to come?<br /> -What tools and techniques are you looking forward to using in the future?<br /> -In what ways do you think data science will continue to transform industries? How Data Science is Preventing College Dropouts and Advancing Student Success tag:www.datasciencecentral.com,2015-11-18:6448529:Video:352169 2015-11-18T02:04:02.136Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/how-data-science-is-preventing-college-dropouts"><br /> <img alt="Thumbnail" height="180" src="https://storage.ning.com/topology/rest/1.0/file/get/2781529886?profile=original&amp;width=240&amp;height=180" width="240"></img><br /> </a> <br></br>Educational institutions have a wealth of data around student demographics, admissions, academic performance and more. In addition to these structured data sources, they also have unstructured data from sources such as student activity on academic discussion forums, campus network access and ID card usage. All of these data sources can be brought together… <a href="https://www.datasciencecentral.com/video/how-data-science-is-preventing-college-dropouts"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781529886?profile=original&amp;width=240&amp;height=180" width="240" height="180" alt="Thumbnail" /><br /> </a><br />Educational institutions have a wealth of data around student demographics, admissions, academic performance and more. In addition to these structured data sources, they also have unstructured data from sources such as student activity on academic discussion forums, campus network access and ID card usage. All of these data sources can be brought together in an institutional data lake to predict and influence student behavior – including attendance patterns, academic performance and time to graduate.<br /> <br /> In this next DSC webinar, two Pivotal data scientists will discuss a recent collaborative project with a top university, in which many data sources were used to build a 360-degree profile of student activity on campus and help predict student success. The session will also provide an overview of the data science pipelines that were developed for training and scoring multiple models in parallel, in-database. These pipelines are now being used to predict student metrics (such as GPA, course grade and time to graduate), and even as intervention tools to help prevent students from dropping out.<br /> <br /> Speakers:<br /> Regunathan Radhakrishnan, Principal Data Scientist -- Pivotal<br /> Srivatsan Ramanujam, Principal Data Scientist -- Pivotal<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central DSC Webinar Series: Data Science Driven Approaches to Malware Detection tag:www.datasciencecentral.com,2015-10-13:6448529:Video:336014 2015-10-13T21:28:50.889Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-science-driven-approaches-to-malware"><br /> <img alt="Thumbnail" height="180" src="https://storage.ning.com/topology/rest/1.0/file/get/2781532809?profile=original&amp;width=240&amp;height=180" width="240"></img><br /> </a> <br></br>Malware detection within enterprise networks is a critical component of an effective information security strategy. A "watering hole" attack is one example of how legitimate websites can be stealthily injected with malware. The malware lies undetected, while redirecting traffic from a legitimate site to a malicious site, which hosts an exploit… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-science-driven-approaches-to-malware"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781532809?profile=original&amp;width=240&amp;height=180" width="240" height="180" alt="Thumbnail" /><br /> </a><br />Malware detection within enterprise networks is a critical component of an effective information security strategy. A "watering hole" attack is one example of how legitimate websites can be stealthily injected with malware. The malware lies undetected, while redirecting traffic from a legitimate site to a malicious site, which hosts an exploit kit that can compromise users' machines. Instances of watering hole attacks are increasing rapidly -- making them especially important to detect.<br /> <br /> In this DSC webinar, one of Pivotal's principal data scientists will discuss data science driven approaches to finding domains that have time and user-based co-occurrence relationships. Developed to find low-support and high-confidence malicious domain associations, these methods aid in the detection of coordinated network intrusions, like watering hole attacks. The session will also demonstrate a scalable and operationalizable framework to detect domain associations by analyzing the web traffic of users in any organization.<br /> <br /> Speaker: Anirudh Kondaveeti, Ph.D. and Principal Data Scientist -- Pivotal<br /> <br /> Hosted by: Bill Vorhies, Editorial Director -- Data Science Central IoT: How Data Science-Driven Software is Eating the Connected World tag:www.datasciencecentral.com,2015-07-21:6448529:Video:303070 2015-07-21T22:49:01.690Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/iot-how-data-science-driven-software-is-eating-the-connected-worl"><br /> <img alt="Thumbnail" height="180" src="https://storage.ning.com/topology/rest/1.0/file/get/2781531484?profile=original&amp;width=240&amp;height=180" width="240"></img><br /> </a> <br></br>The Internet of Things (IoT) will forever change the way businesses interact with consumers and each other. To derive true value from these devices, and ultimately drive the next fundamental shift in how we live and operate, requires the ability to pool this data and build models that drive real and significant actions.<br></br> <br></br> In… <a href="https://www.datasciencecentral.com/video/iot-how-data-science-driven-software-is-eating-the-connected-worl"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781531484?profile=original&amp;width=240&amp;height=180" width="240" height="180" alt="Thumbnail" /><br /> </a><br />The Internet of Things (IoT) will forever change the way businesses interact with consumers and each other. To derive true value from these devices, and ultimately drive the next fundamental shift in how we live and operate, requires the ability to pool this data and build models that drive real and significant actions.<br /> <br /> In this DSC webinar, one of Pivotal's principal data scientists will present a series of use cases illustrating how such devices and the data from these devices drives real impact across industries. From smart sensors to connected hospitals, each example will highlight the fundamental concepts to success.<br /> <br /> You will learn about:<br /> · Starting with the basics: How data science drives action and outcomes<br /> · Avoiding the obstacles: How to avoid the pitfalls that prevent models from driving real action<br /> · Building your toolbox: What tools are available<br /> <br /> The DSC webinar will provide a unique look at new developments in the rapidly-changing world of IoT and data science.<br /> <br /> Panelist: Sarah Aerni, Senior Data Scientist​ -- Pivotal​<br /> Hosted by: Bill Vorhies, Senior Contributing Editor -- Data Science Central