Live Webinar March 10th, 2016 1:00 PM – 2:00 PM EST
Duration: 1 Hour Credits: 1 PDU Category C Free
Presented by : O’Reilly

Machine learning is changing the way organizations look at analytics.

Data scientists are being recognized as a key component in organizational analytics, but management often doesn’t understand their work or know how to effectively manage them.

Many businesses understand that analytics has moved beyond the data warehouse, and are pushing analysts and IT to grab and analyze data from new sources even though they may not be ready to derive business value from these new data.

Open source is seen as the path to machine learning innovation, despite challenges with deployment and approachable user interfaces.

For organizations using or looking to adopt machine learning techniques, moving forward may be a challenge and measuring success even trickier.

In this webcast, Andrew and Patrick will:

• Discuss how different organizations are finding success with machine learning
• Look at how organizations are feeding the creativity of data scientists, providing analytic accessibility to business experts, and pushing the analytics closer to the data.
• Identify how organizations are automating analytic processes in order to free up time for new analytics, new data, and new business problem domains, ultimately creating real competitive advantage.

Presenters:

Andrew Pease (LinkedIn profile) is passionate about getting the most out of data analysis for answering big questions & challenging the status quo. As leader of the analytics team in the SAS Global Technology Practice, Andrew helps organizations to develop & implement analytic roadmaps for combining internal and external data sources, exploiting Hadoop and using high performance analytics to embed analytics in core business processes. Andrew also helps data scientists keep abreast of the latest developments in analytic approaches.

Patrick Hall (LinkedIn profile) is a senior staff scientist at SAS where he designs new data mining and machine learning technologies. He is the 11th person worldwide to become a Cloudera certified data scientist. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University in 2012.

PDU Category C (PMBOK 5) documentation details:
Process Groups: Planning Executing
Knowledge Areas: 5 – Scope 6 – Time 10 – Communications

• 4.2 Develop Project Management Plan
• 5.2 Collect Requirements
• 5.3 Define Scope
• 9.3 Develop Project Team

As a Category C ‘Self Directed Learning Activity’ remember to document your learning experience and its relationship to project management for your ‘PDU Audit Trail Folder’

Click to register for:
How The Machine Learning Wave Is Changing The Way Organizations Look At Analytics