Master Python Debugging From Experts at the University of Michigan School of Information
Sean Corp, Communications Lead
A new online course series helps early career and established data science and programming professionals to write, debug, and understand Python code for real-world data applications.
The four-course series, “Data-Oriented Python Programming and Debugging,” was created by a faculty team from the University of Michigan School of Information and is available on Michigan Online. The series offers learners the flexibility to study at their own pace while gaining valuable expertise in one of the most critical areas of modern computing.
Data science remains one of the most in-demand technology jobs in the market, with 78% of data science job postings in 2023 and 57% in 2024 mentioning Python, according to an analysis from 365 Data Science.
The series is designed to help aspiring and current data scientists who know how to write Python programs but struggle with complex debugging challenges and large-scale data manipulation. The series provides a structured approach to these problems, helping students develop the ability to troubleshoot and refine their code systematically.
“Even though dealing with errors in code is a huge part of programming, there are not a lot of curriculua that deal with this issue,” said Elle O’Brien, lecturer in the School of Information. “This online course series allowed me and my colleagues to take a crack at building the lessons we wished we’d been taught.”
O’Brien is part of a three-member faculty team teaching the series that also includes Paul Resnick, Michael D. Cohen Collegiate Professor of Information, and Anthony Whyte, lecturer, both in the School of Information.
The series also serves as a stepping stone for those considering a graduate degree. Successful completion of the series offers learners a self-paced and self-directed pathway to enrollment in the School of Information’s Master of Applied Data Science program. Those who complete the series, along with a brief admissions interview, can earn advanced standing in the program, allowing them to bypass a four-credit introductory course.
Led by expert instructors Elle O’Brien, Paul Resnick, and Anthony Whyte, the course introduces learners to the OILER framework—Orient, Investigate, Locate, Experiment, and Reflect—a systematic method for identifying and fixing coding errors. Participants will gain hands-on experience with debugging and working with essential data science libraries such as NumPy, pandas, and SciPy. The curriculum covers everything from foundational programming concepts to advanced statistical analysis, preparing learners to tackle real-world data challenges with confidence.
The series is structured around four distinct courses. It begins with an introduction to Python debugging, teaching students how to diagnose and correct errors efficiently. From there, learners dive into the fundamentals of NumPy and pandas, gaining experience in data manipulation and computation. The third course explores statistical techniques using Python, covering key concepts such as probability, data distributions, and hypothesis testing. The final component is a capstone project, where students apply what they have learned to analyze and debug a complex program, demonstrating their mastery of both programming and data science methodologies.
As the demand for skilled data professionals continues to grow, courses like these are helping ensure that students and professionals alike are equipped with the skills required to meet industry demands.
The “Data-Oriented Python Programming and Debugging” series is now open for enrollment on Michigan Online.
Data-Oriented Python Programming and Debugging
In “Data-Oriented Python Programming and Debugging,” you will develop Python debugging skills and learn best practices, helping you become a better data-oriented programmer. Courses in the series will explore how to write and debug code, as well as manipulate and analyze data using Python’s NumPy, pandas, and SciPy libraries. You’ll rely on the OILER framework – Orient, Investigate, Locate, Experiment, and Reflect – to systematically approach debugging and ensure your code is readable and reproducible,…