Mathematical Optimization complements Machine Learning and Statistics in the data scientist's tool belt, but before today's episode with Mathematical Optimization guru Jerome Yurchisin, I knew almost nothing about the powerful technique.

Jerry:
• Works as a Data Science Strategist at Gurobi Optimization, a leading decision-intelligence company that provides mathematical optimization solutions to the likes of Uber, Air France and the National Football League.
• Spent eight years as a mathematical consultant at Booz Allen Hamilton where he paired mathematical optimization with ML, statistics and simulation to inform decision-making.
• Was also previously an instructor at the University of North Carolina at Chapel Hill, where he obtained his Master’s in Operations Research and Statistics.
• Also holds an additional Master’s in Applied Math from Ohio University.

Today’s episode will appeal most to hands-on data science practitioners such as data scientists and ML engineers.

In this episode, Jerry details:
• What mathematical optimization is and how it works.
• Specific real-world examples where mathematical optimization is a better choice than a statistical or machine learning approach.
• His recommended resources for getting started with mathematical optimization in Python (or whatever your preferred programming language is) today.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.