
Madeleinh Ramos developed foundational educational materials for the QLAB-Courses/summer_python_econ repository, focusing on Python data structures, data loading, and object-oriented scaffolding. She created Jupyter Notebooks that guide students through tuples, dictionaries, and lists, incorporating timezone lookups and inventory management examples. Leveraging Pandas, she implemented reproducible workflows for data ingestion, cleaning, and initial analysis, enabling scalable and consistent course exercises. Her work included defining basic Python functions and initial class structures to support future assignments. The engineering approach emphasized code reusability and clarity, providing a robust starting point for student onboarding and reproducible data workflows, with no major bugs reported.

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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