Explorers Series

Unearth the possibilities in your data

Lecture & Lunch

Predictive Analytics for Smarter Business Decision Making

Date: August 27, 2019
Time: 9:00 am - 2:30 pm
Location: Minneapolis Marriott West, 9960 Wayzata Blvd., Minneapolis, MN 55426

Registration & Lunch: Free

Data modeling is mission-critical for achieving success in today’s competitive business environment. All kinds of organizational challenges can now be solved (and even anticipated) by simply looking at the data that’s all around us. Say you want to identify which cost drivers most affect revenue or understand which customers are most likely to upgrade or which lots should be adjusted to avoid waste. 

A well-constructed model will provide you with the much-needed insights that can ultimately help your organization make more strategic, informed decisions. And the most successful models are those that originate with data mining; preparing your data for analysis is just as important as the analysis itself.

In this seminar, Dick De Veaux, PhD, of Williams College will demonstrate how to figure out which data are relevant and identify trends you need to further explore with data visualization. You'll learn best practices for sifting through the extraneous information to pull only the most important variables into an analysis and eliminate missing values before they become problematic. 

Topics include:

  • Identifying relationships and correlations in your data set that have real implications for your work.
  • Exploring your data and prepare data sets for analysis.
  • Designing the methodology that best fits the data you have available.
  • Turning predictive analytics from a job requirement to a professional advantage.

Register now to attend in Minneapolis.

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De Veaux, Dick

Dick De Veaux, PhD, is Professor of Mathematics and Statistics at Williams College and Fellow of the American Statistical Association. He is co-author, with Paul Velleman and David Bock, of several critically acclaimed textbooks, including Intro Stats and Stats: Data and Models.

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