DSC Webinar Series: Adding Optimization to Your Analytics Toolbox

Mathematical optimization, specifically Mixed Integer Programming (MIP), is a technology that is used to solve a large variety of problems within multiple industries, including supply chain planning, electrical power generation and distribution, computational finance, sports scheduling, and many more. This powerful technology is complementary to Machine Learning and should be a part of every data scientist’s analytics toolbox.

In this latest Data Science Central webinar, you will learn:

The basics of optimization and MIP
How to identify optimization problems within your organization
When to use MIP vs Artificial Intelligence (AI) when developing a prescriptive analytics solution for your business problem
How MIP can be used as a complementary technique to Machine Learning
We will present real-world examples of Machine Learning and optimization in action, illustrating the value it can bring to your organization. We will also provide you with next steps on how to get started with optimization as well as available resources.

Dr. Russell Halper, Principal - End-to-End Analytics
Dr. Gwyneth Butera, Sr. Support Engineer - Gurobi Optimization

Hosted by:
Rafael Knuth, Contributing Editor - Data Science Central

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Comment by Shawn Hainsworth on June 5, 2019 at 4:56pm

This is a terrific presentation.  Dr. Butera does a great job of introducing the mathematics and algorithms used to solve optimization problems and Dr. Halper does an equally great job connecting optimization to relevant business problems and using optimization with machine learning.  I will never complain about the NFL schedule again.  Who codes all of those constraints?  Thanks to you both!

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