During December 2024, Sathe Venkatesh contributed to the nikbearbrown/INFO_7390_Art_and_Science_of_Data repository by developing three core features that enhance data science workflows and user accessibility. He created a Jupyter Notebook applying causal inference and exploratory data analysis to heart disease data, using Python, Pandas, and Seaborn for preprocessing, visualization, and correlation analysis. Sathe also authored a comprehensive PDF resource on supply chain management, offering practical guidance for business users. Additionally, he updated project documentation to include live app and demo links, improving onboarding. His work demonstrated depth in data analysis, document management, and clear, traceable documentation practices.
December 2024 (nikbearbrown/INFO_7390_Art_and_Science_of_Data): Delivered three core assets to advance data science capabilities and user accessibility: - Causal Inference Notebook for Heart Disease Analysis: Jupyter notebook detailing causal inference concepts, preprocessing, EDA, visualization, and predictor correlation for a heart-disease dataset. - Supply Chain Management Resource Document: A comprehensive PDF covering supply chain design approaches, lean/agile practices, and purchasing strategies for business users. - Documentation Update: Live App and YouTube Demo Links: Updated README with current live app URL and YouTube demo link to improve user onboarding. No major bugs fixed this month. Business value: accelerates data-driven healthcare analysis, provides a practical, audited reference for supply chain decision-making, and enhances deployment transparency and user onboarding. Technologies/skills demonstrated: Jupyter notebooks, data preprocessing, EDA, visualization, correlation analysis, document authoring, and README/documentation best practices with commit-level traceability.
December 2024 (nikbearbrown/INFO_7390_Art_and_Science_of_Data): Delivered three core assets to advance data science capabilities and user accessibility: - Causal Inference Notebook for Heart Disease Analysis: Jupyter notebook detailing causal inference concepts, preprocessing, EDA, visualization, and predictor correlation for a heart-disease dataset. - Supply Chain Management Resource Document: A comprehensive PDF covering supply chain design approaches, lean/agile practices, and purchasing strategies for business users. - Documentation Update: Live App and YouTube Demo Links: Updated README with current live app URL and YouTube demo link to improve user onboarding. No major bugs fixed this month. Business value: accelerates data-driven healthcare analysis, provides a practical, audited reference for supply chain decision-making, and enhances deployment transparency and user onboarding. Technologies/skills demonstrated: Jupyter notebooks, data preprocessing, EDA, visualization, correlation analysis, document authoring, and README/documentation best practices with commit-level traceability.
Overview of all repositories you've contributed to across your timeline