Lecturer IV and Research Investigator
Python Debugging Capstone Project: Fixing and Extending Code
What You'll Learn
- Independently debug a variety of code issues.
- Interpret and implement evolving project requirements.
- Import, clean, and manipulate data acquired from remote sources.
- Deliver notebooks that can be read, run, and reproduced.
2 Modules
16 Hours
8 hrs per module (approx.)
Rating
About Python Debugging Capstone Project: Fixing and Extending Code
In “Python Debugging Capstone Project: Fixing and Extending Code,” you will undertake a comprehensive coding project that demonstrates your ability to use NumPy, pandas, and SciPy for advanced data manipulation and scientific computing. You will use the skills and best practices learned throughout the series to debug and analyze real-world data. This project will involve working with complex datasets, requiring you to implement data structures and handle missing data.
You will engage in systematic debugging using the OILER framework – Orient, Investigate, Locate, Experiment, and Reflect – learned in the first course within the series and other advanced troubleshooting techniques. By the end of the course, you will know how to properly document your processes through clean, maintainable code notebooks.
Skills You'll Gain
- Data Analysis
- Data Manipulation
- 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 Capstone Project: Fixing and Extending Code. Learners independently debug, refactor, and extend existing Python code while producing reproducible, well-documented Jupyter notebooks using real-world datasets.
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: Project Briefing
- Video: Welcome to the Course and Specialization
- Reading: Course Syllabus
- Discussion Prompt: Introduce Yourself
- Reading: Help Us Learn About You
- Video: Welcome to 'Course 4'
- Video: Your Briefing
- Reading: Railroad Data Collection Methodology Report
- Reading: Pandas Review
- Reading: NumPy Review
- Reading: Review of Framework
- Reading: Structuring data science projects
- Reading: Why Does Project Structure Matter?
- Graded Assignment: Reflect on Your Progress
- Graded: Assessment 1.1
- Graded: Assessment 1.2
- Graded: Assessment 1.3
Module 2: Updated Requirements and Final Deliverable
- Video: New Briefing
- Reading: Documenting Your Work
- Video: Outro for series
- Reading: Post Course Survey
- Reading: You Are One Step Closer to Earning a Master's Degree from University of Michigan
- Reading: Attributions
- Graded: Assessment 2.1
- Graded: Assessment 2.2
- Graded: Assessment 2.3
- Graded: Assessment 2.4
- Graded: Assessment 2.5
Grading Policy
There are three programming assessments in Module 1, each worth 16.8% of your final grade. There are five programming assessments in Module 2, each worth 10% 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.
