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Slides: Enhance Predictive Modeling with Better Data Preparation

By   /  April 8, 2016  /  No Comments

To view the On Demand recording of this presentation, click HERE>>

This webinar was sponsored by:

alteryx

About the Webinar

Predictive modeling can be tough to implement in itself. Additionally, increasing data types and sources are making the data preparation process more complex too. Experts know that 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 live webinar, we will walk you through the steps to build the right dataset for more accurate predictive modeling. You’ll learn how to:

  • Access the quality of your data
  • Cleanse and prepare data for analysis
  • Decide what predictive modeling techniques to use

Join the webinar to get practical advice on making data preparation and predictive analytics more accessible throughout your organization.

About the Speakers

Ritu Jain

Director, Industry Marketing, Alteryx

Ritu Jain.jpgRitu has over 18 years of experience in retail supply chain, both as a practitioner and a technology marketer. Most recently, she spent over 12 years leading global marketing strategy for various markets and industries including retail, supply chain and small and midsize business at SAS Institute. Prior to SAS, she held a number of positions in retail supply chain management leading strategic planning, operations, and production management with William E. Connor, and Associates, a global sourcing partner to prominent retailers such as Dillard’s and Pottery Barn.

Dan Putler

Chief Scientist, Alteryx

dan_putler.jpgDr. 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.

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