Data-Oriented Python Programming and Debugging
Description
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.
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Subject
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Language
English
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Duration
16 weeks
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Status
Available
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U-M Credit Eligible
No
Data Science
Instructors
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Elle O'Brien
Lecturer IV and Research Investigator
University of Michigan, School of Information
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Paul Resnick
Michael D. Cohen Collegiate Professor of Information
University of Michigan, School of Information
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Anthony Whyte
Lecturer
University of Michigan, School of Information
Courses (4)
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Learn morePython Debugging: A Systematic Approach
In “Python Debugging: A Systematic Approach,” you will develop essential coding skills for data science, … -
Learn moreNumPy and Pandas Basics for Future Data Scientists
In “NumPy and Pandas Basics for Future Data Scientists,” learn programming techniques using Python's NumPy … -
Learn moreStatistics with Python Using NumPy, Pandas, and SciPy
“Statistics with Python Using NumPy, Pandas, and SciPy” explores how to apply statistical and mathematical … -
Learn morePython Debugging Capstone Project: Fixing and Extending Code
In “Python Debugging Capstone Project: Fixing and Extending Code,” you will undertake a comprehensive coding …