Python Debugging: A Systematic Approach

In “Python Debugging: A Systematic Approach,” you will develop essential coding skills for data science, focusing on writing, testing, and debugging code. You will learn foundational Python concepts, such as looping, control structures, variables, and basic debugging techniques. You will also learn how a structured debugging procedure can help you debug more effectively and efficiently. Throughout the course, you’ll practice essential programming concepts such as map, filter, and list comprehension. You’ll learn how to take a systematic approach to debugging with the OILER framework – Orient, Investigate, Locate, Experiment, and Reflect – allowing you to spot errors more easily and adjust your code. In addition to frameworks to help you improve your code, you’ll explore how documentation, internet resources, and even large language models (LLMs) can help you identify and fix errors. By the end of this course, you should feel confident in your abilities to write clean, efficient, and reusable code. This is the first course in the four-course series, “Data-Oriented Python Programming and Debugging,” where you’ll work to strengthen your programming capabilities and enhance your problem-solving skills

• Use Jupyter Notebook to implement basic Python workflows
• Apply the OILER framework to debug common Python bugs
• Use official Python documentation to understand programming formats
• Interpret Python error messages to resolve execution issues

• Basic familiarity with the topic and terminology
• Readiness to practice through applied exercises or case studies