
Over three months, contributed to the ksgeist/Merrimack_DSE6630 repository by developing end-to-end data science workflows for clinical and biomedical analytics. Built reproducible pipelines for pneumonia readmission risk and RNA-seq analysis, integrating data cleaning, preprocessing, feature engineering, and advanced modeling using R and SQL. Implemented spatial regression frameworks with shapefile and population data integration, and enhanced RNA-seq workflows with robust quality filtering and improved visualizations. Strengthened documentation and code clarity through R Markdown, enabling transparent reporting and stakeholder decision support. Addressed metadata parsing issues and stabilized runtime processes, ensuring reliable, interpretable results across healthcare analytics and bioinformatics use cases.
July 2025 — ksgeist/Merrimack_DSE6630: Delivered targeted improvements to the RNA-seq analysis workflow and its visualizations, emphasizing reproducibility, clarity, and decision-support for stakeholders. This work focused on code quality, robust data processing, and transparent reporting.
July 2025 — ksgeist/Merrimack_DSE6630: Delivered targeted improvements to the RNA-seq analysis workflow and its visualizations, emphasizing reproducibility, clarity, and decision-support for stakeholders. This work focused on code quality, robust data processing, and transparent reporting.
June 2025: Delivered a robust spatial analytics workflow for Merrimack_DSE6630 to analyze regional hospital readmission drivers by integrating population data and shapefiles. Implemented end-to-end support for four spatial regression models (OLS, SAR, SEM, SDM) with data loading, preprocessing, model fitting, interpretation, and cross-model comparison. Strengthened data preprocessing with imputation and Box-Cox transformations, and deepened analysis of feature relationships including multicollinearity checks and correlations with the target variable. Improved project documentation with clearer shapefile explanations, cross-references, and readable R Markdown sections related to spatial analysis and data joins. Stabilized the workflow by addressing runtime messages and completing Q1-22 coverage (including Q20C), enhancing reproducibility and reliability.
June 2025: Delivered a robust spatial analytics workflow for Merrimack_DSE6630 to analyze regional hospital readmission drivers by integrating population data and shapefiles. Implemented end-to-end support for four spatial regression models (OLS, SAR, SEM, SDM) with data loading, preprocessing, model fitting, interpretation, and cross-model comparison. Strengthened data preprocessing with imputation and Box-Cox transformations, and deepened analysis of feature relationships including multicollinearity checks and correlations with the target variable. Improved project documentation with clearer shapefile explanations, cross-references, and readable R Markdown sections related to spatial analysis and data joins. Stabilized the workflow by addressing runtime messages and completing Q1-22 coverage (including Q20C), enhancing reproducibility and reliability.
Summary for 2025-05: In ksgeist/Merrimack_DSE6630, delivered a new pneumonia-focused analysis workflow and stabilized metadata parsing, strengthening the data-to-model pipeline for clinical informatics and hospital readmission risk assessment. The work enables reproducible analyses and faster insight generation for predictive modeling.
Summary for 2025-05: In ksgeist/Merrimack_DSE6630, delivered a new pneumonia-focused analysis workflow and stabilized metadata parsing, strengthening the data-to-model pipeline for clinical informatics and hospital readmission risk assessment. The work enables reproducible analyses and faster insight generation for predictive modeling.

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