Contributed three core features to the INFO_7390_Art_and_Science_of_Data repository, focusing on data science workflows and user accessibility. Developed a Jupyter Notebook applying causal inference techniques to heart disease data, incorporating preprocessing, exploratory data analysis, visualization, and correlation analysis using Python, Pandas, and Seaborn. Authored a comprehensive PDF resource on supply chain management, detailing design approaches and purchasing strategies for business users. Enhanced project documentation by updating the README with live application and YouTube demo links, streamlining user onboarding. The work demonstrated depth in data analysis, document management, and technical communication, providing practical assets for both technical and business stakeholders.
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.
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