Lecturer IV and Research Investigator
Python Debugging: A Systematic Approach
What You'll Learn
- Use Jupyter Notebook to implement basic Python workflows and constructs.
- Apply the OILER framework for debugging many common Python bugs.
- Use official Python documentation to enhance understanding of different programming formats.
- Interpret Python error messages to resolve runtime execution issues.
4 Modules
20 Hours
5 hrs per module (approx.)
Rating
About 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.
Skills You'll Gain
- Code Testing
- Computer Programming
- Data Analysis
- Debugging
- Python For Data Analysis
- Python (Programming Language)
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Python Debugging: A Systematic Approach, a hands-on course that teaches learners how to identify, analyze, and resolve coding issues using a structured debugging framework. Learners practice debugging in Jupyter notebooks and build maintainable, reproducible code.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Review and Setup
- Video: Welcome to the Course and Specialization
- Reading: Course Syllabus
- Discussion Prompt: Introduce Yourself
- Reading: Help Us Learn About You
- Video: Welcome to Course 1
- Ungraded Lab: Jupyter Lab Environment
- Video: Intro to Jupyter Notebook
- Reading: Course Content Disclaimers
- Reading: Recommender Systems
- Video: Review: Map, Filter, List Comprehensions
- Ungraded Lab: Jupyter Lab 1
- Graded: Assessment 1
Module 2: The Debugging Framework
- Ungraded Lab: Jupyter Lab Environment
- Video: Overview of the Debugging Framework
- Video: Orient Yourself
- Video: Investigate the Symptoms Part I: Understanding Error Messages
- Video: Investigate the Symptoms Part II: Code vs. Intention Mismatch
- Video: Investigate the Symptoms Part III: Misunderstanding Computations and Data
- Video: Locate the Root Cause
- Video: Experiment With a Fix
- Video: Reflect
- Reading: Oiler Framework: Part 1 - Chapters 1-6
- Ungraded Lab: Jupyter Lab 2
- Graded: Assessment 2.1
- Graded: Assessment 2.2
Module 3: Framework Skills
- Ungraded Lab: Jupyter Lab Environment
- Video: Framework Example 2
- Video: Framework Example 3
- Reading: Oiler Framework: Part 2 - Chapters 7-12
- Ungraded Lab: Bug Log and Bug Recipes
- Video: Debugging with an LLM
- Video: Debugging with the Internet
- Video: Debugging with Docs
- Video: Using the Jupyter Debugger
- Ungraded Lab: Jupyter Lab 3
- Graded: Assessment 3
Module 4: Stop Bugs Before they Happen
- Ungraded Lab: Jupyter Lab Environment
- Video: Introduction: Make it Easy on Future You
- Video: DRY (Don't Repeat Yourself) Part 1: Iterate
- Video: DRY (Don't Repeat Yourself) Part 2: Write a Function
- Video: Documenting Functions
- Video: Making Your Notebook Reproducible
- Reading: Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks
- Reading: Post Course Survey
- Reading: Attributions
- Graded: Assessment 4.1
- Graded: Assessment 4.2
Grading Policy
Learners must earn an overall grade of 80%. Assessments in each module account for 25% of your final grade.
Michael D. Cohen Collegiate Professor of Information
Lecturer IV
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners should complete "Python 3 Programming" on Coursera or have equivalent experience with Python programming basics.
