English | Size: 6.4 GB
Genre: eLearning
Write and systematically debug Python code. Develop readable and reproducible Python code 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.
Specialization – 4 course series
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.
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.
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