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Jul 18 Webinar: The Art and (Data) Science of Data Cleansing and Quality

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DATE: July 18, 2017 This webinar has passed. The recording will be made available On Demand within two US business days.

TIME: 2 PM Eastern / 11 AM Pacific

PRICE: Free to all attendees

This Webinar is Sponsored by:

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

Dan Putler

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.

Lisa Aguilar

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.

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