On-Demand Webinar

Analytics 101 (Part 3)

Join us as we discuss best practices for the application and deployment of predictive modeling.

About the webinar

The Analytics 101 series is for professionals interested in building or advancing an analytics culture within their organization.

In part one of three, we introduced you to a road map to navigate the analytics landscape and discussed the variety of techniques available to uncover meaningful insight from your data.

In part two, we focused on analyzing unstructured data as the fastest growing source of data and unique insights.

In part three, the focus will be on the application and deployment of predictive modeling. Modeling enables organizations to make data-driven decisions. Operationalizing these models is key to maximizing the value to your organization.

What we'll cover:

  • Efficiently building accurate predictive models using machine learning techniques.
  • Deploying and managing models to implement predictions within business processes.
  • Integrating open source models to expand your analytics ecosystem.

Have a SAS profile? To complete this form automatically Sign In

*
*
*
*
 
*
*

All personal information will be handled in accordance with the SAS Privacy Statement.

 
  Yes, I would like to receive occasional emails from SAS and SAS business partners about their products and services. I understand I can withdraw my consent at any time by clicking the opt-out link in the emails.
 
 

About our expert guests


Sharon Walker
Systems Engineer, SAS

Sharon Walker is a Systems Engineer at SAS, focusing on the use of analytics and machine learning tools in small and midsize businesses. Prior to SAS, she worked in a variety of industries, including software, telecommunications, financial services, online retail and pharmaceuticals. Sharon holds a master’s in analytics from NC State University and an MBA from the Wharton School of Business.


Sal Ciaravino
Systems Engineer, SAS

Sal Ciaravino is a SAS Systems Engineer supporting SMB with a focus on machine learning. Sal has a master’s degree in applied statistics from the University of Alabama, where he was first introduced to the power of predictive analytics using SAS software.

Register now