[Webcast] Detecting Anomalies in IoT with Time Series Analysis - July 26th, 2016

Document created by Dolletta.Mitchell@Teradata.com on Jul 8, 2016Last modified by Dolletta.Mitchell@Teradata.com on Jul 20, 2016
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Start Date:  July 26, 2016

Start Time:  10AM PT, San Francisco

1pm - New York | 6pm - London | 10:30pm - Mumbai |Wed, Jul 27th at 1am - Beijing|Wed, Jul 27th at 2am - Tokyo | Wed, Jul 27th at 3am - Sydney

Duration:    60 minutes

 

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Description:

Anomaly detection is used widely to perform various tasks such as fraud detection in the financial industry, network breach for cyber-security, and enemy surveillance for the military. Data scientists apply various models to find anomalies, using a range of techniques from statistics to machine learning. However, the explosion of time series data generated by the Internet of Things (IoT) has made this task more challenging than ever. In the area of predictive maintenance, for example, data scientists struggle with hundreds of sensors that generate data every millisecond in order to find the unwanted noise that may lead to machine failure. Now, data scientists not only need to find the right algorithms to apply in these massive data sets, but they also need to find the right tools to handle big data.

 

Join us for a live webcast where you will learn how to:

  1. Overcome common challenges in finding anomalies in time series data
  2. Apply various statistical and machine learning algorithms in time series data
  3. Differentiate event-based anomaly detection and pattern-based anomaly detection
  4. Use tools that provide multi-genre analytics capability, such as Teradata Aster, to tackle anomaly detection

 

Presented by: Cheryl Wiebe and Todd Morley

Presenters:

cheryl_wiebe.pngCheryl Wiebe,  Partner, Analytics of Things

Teradata

 


About Cheryl Wiebe

Cheryl Wiebe leads the Manufacturing Applied Analytics team for Teradata's consulting business, with the mission to "operationalize analytics" at scale within manufacturing customers, such as P&G, Coca-Cola, Caterpillar, Ford, Flextronics, GE Healthcare, Boeing, Georgia Pacific. Cheryl’s previous positions at Teradata include: Consulting Partner, west coast M&E and digital media/ecommerce clients, where she oversaw the deployment of a wide variety of analytics and machine learning solutions for our customers, especially eBay, eBay Enterprise, Groupon, PayPal and prior to that, Cheryl was a Director, Consulting for Supply Chain Analytics, Coe for Demand and Supply Chain.

todd_morley.pngTodd Morley, Senior Data Scientist

Teradata

 


About Todd Morley

Todd Morley, is a senior data scientist in Teradata's Advanced Analytics COE, and the architect of VAP. His book Data Science Design Patterns, will be released soon by Addison Wesley. He has taught graduate data science and data architecture at UCCS and has written over a half-million lines of code.

 

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