Write and systematically debug Python code. . Learn to develop readable and reproducible code in Python while investigating, manipulating, and analyzing real-world data using Python libraries.

What you'll learn

Effective use of modules, functions, and object methods in data-driven computing.
Competent independent debugging and self-help skills in Python.
Proficient programming with common data structures such as arrays and DataFrames using libraries like NumPy and pandas.
Skills you'll gain
Statistics
Python (Programming Language)
SciPy
Debugging
NumPy
Data Analysis
Critical Thinking
Pandas (Python Package)
Data Manipulation
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.
Applied Learning Project
Perform Python debugging assignments on real data sets using Jupyter notebooks. Build your skills through debugging challenges and practice labs, using data sets from sports, weather, movies, and more. Complete a capstone project showcasing your skills as a data-oriented debugger using a transportation data set.
Homepage:

https://www.coursera.org/specializations/data-oriented-python-programming-and-debugging

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