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Data Science and ML

Power to the Data Report Podcast: The Math Behind the Models

Here I am delivering the keynote for the UCLA Math Dept. 2023 Commencement.

Hello, and welcome to the “Power-to-the-Data Report” podcast where we cover timely topics of the day from throughout the Big Data ecosystem. I am your host Daniel Gutierrez from insideBIGDATA where I serve as Editor-in-Chief & Resident Data Scientist.

Today’s topic is “The Math Behind the Models,” one of my favorite topics when I’m teaching my Introduction to Data Science class at UCLA. In the podcast, I’ll discuss how in the age of data-driven decision-making and artificial intelligence, the role of data scientists has become increasingly vital. However, to truly excel in this field, data scientists must possess a strong foundation in mathematics and statistics. These disciplines are the bedrock upon which the entire field of data science is built. In this article, we will explore why data scientists should prioritize learning mathematics and statistics, discussing the numerous benefits it brings and how it enhances their ability to extract meaningful insights from vast amounts of data.

In the podcast I reference “nomadic data science,” please see HERE if you’re intrigued. Also, here is an excellent learning resource for establishing a foundation for the math behind the models: “Mathematics for Machine Learning,” by Deisenroth, Faisal, and Ong. Once you’ve consumed this text, you’ll be ready for “Elements of Statistical Learning,” the ML bible, the book you never finish reading. Another one of my favorites is “Math for Deep Learning,” by Ronald T. Kneusel from No Starch Press.

Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies. 

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