About this Course
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
What You'll Learn
- Use modules, functions, and object methods effectively in data-driven programming
- Debug code independently and develop self-help skills in Python
- Program proficiently with common data structures like arrays and DataFrames
- Utilize NumPy and pandas libraries efficiently
Prerequisites
- Basic familiarity with topic terminology
- Readiness to practice through applied exercises or case studies
Instructors
Anthony Whyte
Lecturer IV
Paul Resnick
Michael D. Cohen Collegiate Professor of Information
Elle O'Brien
Lecturer IV & Research Investigator