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About the Webinar
By itself, predictive modeling can be difficult to implement. What’s more, the growing number of data types and sources are making the data preparation process more complex. While predictive modeling can deliver substantial business gains, it can also wreak havoc if the data used for analysis is not accurate or complete.
In this session, we will walk you through the steps to build the right dataset for more accurate predictive modeling. You’ll learn how to:
- Assess the quality of your data
- Cleanse and prepare data for analysis
- Decide what predictive modeling techniques to use for your specific situation
Join the webinar to get practical advice on making data preparation and predictive analytics more accessible throughout your organization.
About the Speakers
Chief Scientist, Alteryx
Dr. Dan Putler is the Chief Scientist at Alteryx, where he is responsible for developing and implementing the product road map for predictive analytics. He has over 30 years of experience in developing predictive analytics models for companies and organizations that cover a large number of industry verticals, ranging from the performing arts to B2B financial services. He is co-author of the book, “Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R”, which is published by Chapman and Hall/CRC Press. Prior to joining Alteryx, Dan was a professor of marketing and marketing research at the University of British Columbia’s Sauder School of Business and Purdue University’s Krannert School of Management.
Sr. Product Marketing Manager, Alteryx
Lisa Richardson-Aguilar is a Sr. Product Marketing Manager at Alteryx. She is passionate about the analytics space and showing how innovative analytic technology can help analysts move past mundane data tasks, elevate their skills and expertise, and deliver ever increasingly sophisticated insights.