Matt is a seasoned Python and Data Science expert with over a decade of experience, known for his passion for making complex programming and data analysis concepts accessible and engaging. A Stanford University graduate with a background in Computer Science, Matt has applied his skills across a wide range of domains—from building predictive models that classify job postings to developing scalable systems using AWS and Docker.

Matt’s expertise spans machine learning, natural language processing, web application development, and beyond. He has a proven track record of translating technical depth into impactful educational experiences. As an instructor, he has designed and delivered high-impact courses such as Python for Finance and Essentials of Stats with Python, enabling learners to grasp both the theoretical foundations and practical applications of programming and data science.

A strong advocate for critical thinking and problem-solving, Matt creates inclusive learning environments where students from all backgrounds can thrive. His dedication to education extends to the global stage, where he regularly speaks at conferences like PyCon and SciPy and contributes to leading platforms including O’Reilly Media and Pluralsight.

With a teaching philosophy grounded in authenticity and real-world relevance, Matt is committed to empowering the next generation of data scientists and programmers—bridging the gap between theory and application, and inspiring learners to unlock the full potential of Python and data science.