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, ensuring you produce high-quality code in all of your projects. The series concludes with a capstone project, where you’ll use these skills to debug and analyze a real-world data set, showcasing your skills in data manipulation, statistical analysis, and scientific computing.
JT
This course delivers on the title and covers the basics of NumPy and Pandas.
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Good material with a ill-coded auto-grader. The auto-grader will mark your answer as wrong if the difference in results is the 16th digit after the decimal point. At least the passing grade is not set very high so eventually you will pass the assessment. Just don't over-commit, getting a pass in those assignments is already good enough
I was deducted two points because of the following AssertionError: The average rate of change for Paul's 'Recommender Systems' publication must equal 0.21062437251345786. My result was 0.2106243725134579 (calculated by computer not by my calculator!) This is maddening. I don't know if I am ready to continue with this course.