655: AI ROI: How to get a profitable return on an AI-project investment — with Keith McCormick

Educational Gaps

The current data science education system often emphasizes theoretical knowledge over practical skills, leaving graduates unprepared for real-world challenges. Keith McCormick argues that while advanced topics like calculus and chemistry are fascinating, they rarely translate into practical skills needed in the field 1. He questions why the educational model for data science is based on the needs of tech giants like Netflix, which require custom algorithms, rather than on smaller organizations that need practical solutions 2.

It's insane to me. It just doesn't make any sense. Now, I'm not saying across the board that you shouldn't solve those problems with code, or that really knowing the behind the scenes, but for me, knowing the history of the algorithms and how they work is sufficient to manipulate the hyper parameters and so on.

--- Keith McCormick

Jon Krohn adds that the imbalance in education is akin to teaching abstract skills that most people will never use in their careers 3.

   

Practical Experience

Practical experience is crucial in data science education, yet often overlooked. Keith McCormick emphasizes the importance of understanding the entire machine learning lifecycle, not just modeling, to manage projects effectively and collaborate with clients 4. He highlights the need for a residency-like experience in data science, similar to medical training, to bridge the gap between academic learning and industry demands 5.

I think we have a whole generation of data scientists that are, well, now I think I'm oversimplifying because it was true when I started out, too, but we've always had an obsession with modeling and modeling algorithms and haven't understood enough how you get a project from beginning to end.

--- Keith McCormick

This approach would prepare data scientists to scope projects and write client contracts, skills often missing from current curricula.