Worked on enhancing the reliability and clarity of Jupyter notebooks in the d2cml-ai/CausalAI-Course and d2cml-ai/Data-Science-Python repositories. Focused on improving tutorial content by correcting code snippets, resetting execution counts, and refactoring cells to ensure accurate outputs, thereby supporting a smoother learning experience. Standardized kernel and Python version configurations, specifically updating Folium.ipynb to Python 3.11.13, to promote reproducibility across environments. Leveraged Python, Pandas, and Jupyter Notebook configuration skills to reduce setup friction for both learners and instructors. Demonstrated attention to detail and cross-repository collaboration, aligning technical environments and outputs to improve data science education quality.