Python for Data Science
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Thanks for registering to our Python for Data Science course event. We will add your name to our event list, and you will receive a confirmation email from the University of Chicago shortly.
Learning Python is not just about completing a course.
It is about gaining a tool you can use throughout your career to explore data, test ideas, and understand the systems behind modern analytics and AI.
Professionals across industries use Python to:
- Analyze research and operational data.
- Understand customer behavior and business trends.
- Build predictive models and machine learning systems.
- Explore and evaluate AI-driven tools.
In this course, you will not just learn the concepts—you will apply them directly to real datasets while working alongside experienced practitioners who use Python in their own data science work.
That combination of hands-on practice and expert guidance helps transform Python from an abstract programming language into a practical tool you can use in your own projects.
Because Python is used across industries—including technology, finance, healthcare, consulting, and research—the skills you develop can travel with you from one role to the next.
For many professionals, learning Python changes how they approach their work.
Instead of waiting for others to analyze information, they gain the ability to explore questions directly, test ideas quickly, and uncover insights on their own.
Meet your instructor
Michael Colella, MS, MA, instructor for the Python for Data Science course.
Michael is Senior Director of Global Data Strategy and Analytics at AXS, where he leads teams responsible for business intelligence, analytics engineering, and web analytics.
Before joining AXS, he served as a chief data scientist and technology leader at Boston Consulting Group’s advanced analytics group (BCG Gamma) and has held senior data leadership roles across major organizations. Earlier in his career, Michael worked in genetics and cognitive neuroscience research before moving into data science—an experience that gives him a unique perspective on how professionals from many backgrounds can transition into data-driven work.