
Developed foundational educational materials for the QLAB-Courses/summer_python_econ repository, focusing on Python data structures, data cleaning, and object-oriented scaffolding within Jupyter Notebooks. Leveraged Python and Pandas to create reproducible workflows for data loading, filtering, and feature engineering, supporting scalable course exercises and streamlined student onboarding. Implemented tutorials demonstrating tuples, dictionaries, and lists, along with timezone lookups and inventory management techniques. Delivered assignment solutions that introduced basic function definitions and initial class structures, laying groundwork for future exercises. Emphasized code reusability and clarity through object-oriented patterns and utility functions, establishing a robust, maintainable teaching stack for ongoing course development.
For January 2025, delivered foundational educational notebooks in QLAB-Courses/summer_python_econ, focusing on core Python data structures, data loading/cleaning with Pandas, and initial object-oriented scaffolding. The work creates a reusable teaching stack and a reproducible data workflow that accelerates student onboarding and supports scalable course exercises. Major bugs fixed: none reported in this period.
For January 2025, delivered foundational educational notebooks in QLAB-Courses/summer_python_econ, focusing on core Python data structures, data loading/cleaning with Pandas, and initial object-oriented scaffolding. The work creates a reusable teaching stack and a reproducible data workflow that accelerates student onboarding and supports scalable course exercises. Major bugs fixed: none reported in this period.

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