
Sue Susman contributed to the ksgeist/Merrimack_DSE6630 repository by developing and refining data preparation pipelines and analytics modules for healthcare datasets, including hospital readmission and pneumonia analysis. She applied R and Tidyverse to clean, merge, and transform raw data, enabling robust feature engineering and more interpretable visualizations. Her work included expanding exploratory data analysis in R Markdown, improving metadata and documentation, and reorganizing project files for better collaboration. By addressing data quality and repository hygiene, Sue reduced onboarding time and improved report accuracy, supporting transparent, data-driven decision-making for stakeholders. Her contributions demonstrated depth in data wrangling and statistical analysis.
June 2025 performance summary for ksgeist/Merrimack_DSE6630: Delivered core data preparation and analytics enhancements that enable reliable pneumonia dataset analysis and clearer hospital readmission insights, while improving repository organization and documentation. These efforts reduce data wrangling time, improve report accuracy, and strengthen collaboration, accelerating data-driven decision-making and operational transparency for stakeholders.
June 2025 performance summary for ksgeist/Merrimack_DSE6630: Delivered core data preparation and analytics enhancements that enable reliable pneumonia dataset analysis and clearer hospital readmission insights, while improving repository organization and documentation. These efforts reduce data wrangling time, improve report accuracy, and strengthen collaboration, accelerating data-driven decision-making and operational transparency for stakeholders.
May 2025 monthly summary focusing on key accomplishments, business value, and technical achievements across ksgeist/Merrimack_DSE6630. Highlights include healthcare analytics data cleaning and prep for hospital readmission data enabling robust feature engineering and clearer demos; expanded metadata, objectives, and EDA coverage in Project_1.Rmd; team onboarding and documentation updates; and repository hygiene improvements to ignore temporary R data files. Technologies demonstrated include R, R Markdown, data wrangling/ETL, metadata design, exploratory data analysis, and Git governance. Overall impact: accelerated feature delivery, more transparent demonstrations for stakeholders, and improved collaboration and governance across the project.
May 2025 monthly summary focusing on key accomplishments, business value, and technical achievements across ksgeist/Merrimack_DSE6630. Highlights include healthcare analytics data cleaning and prep for hospital readmission data enabling robust feature engineering and clearer demos; expanded metadata, objectives, and EDA coverage in Project_1.Rmd; team onboarding and documentation updates; and repository hygiene improvements to ignore temporary R data files. Technologies demonstrated include R, R Markdown, data wrangling/ETL, metadata design, exploratory data analysis, and Git governance. Overall impact: accelerated feature delivery, more transparent demonstrations for stakeholders, and improved collaboration and governance across the project.

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