jdpipping/summer-lab
Jun 2025 – Jun 2025
1 Month active
Languages Used
RStan
Technical Skills
Bayesian InferenceData AnalysisData VisualizationEmpirical BayesMachine LearningR Programming
Noahsonnenklar
PROFILE
Noahsonnenklar
Noah developed a suite of reproducible R Markdown labs for the jdpipping/summer-lab repository, advancing sports analytics education through data-driven projects. He implemented five labs covering NBA, NFL, and NCAA topics, focusing on empirical Bayes modeling, linear and logistic regression, and data visualization. Using R and Stan, Noah emphasized robust statistical methods and reproducible workflows, enabling scalable classroom templates for analyzing player performance, team strengths, and game outcomes. His work demonstrated depth in Bayesian inference and statistical modeling, providing clear, actionable insights for sports analytics while ensuring the labs were accessible, well-documented, and adaptable for educational and research purposes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
5
Lines of code
1,676
Activity Months1
Your Network
38 people
Work History
June 2025
6 Commits • 5 Features
Jun 1, 2025June 2025 — Delivered a cohesive set of reproducible R Markdown labs in jdpipping/summer-lab that advance sports analytics education and analytics capabilities. Implemented five data-driven labs spanning NBA/NFL/NCAA topics, empirical Bayes trajectories, and Bayesian team-strength modeling, with an emphasis on data loading, visualization, and robust statistical methods. These efforts provide scalable, classroom-ready templates that enable data-driven decisions and deeper insights for sports analytics.
6 Commits • 5 Features
Jun 1, 2025June 2025 — Delivered a cohesive set of reproducible R Markdown labs in jdpipping/summer-lab that advance sports analytics education and analytics capabilities. Implemented five data-driven labs spanning NBA/NFL/NCAA topics, empirical Bayes trajectories, and Bayesian team-strength modeling, with an emphasis on data loading, visualization, and robust statistical methods. These efforts provide scalable, classroom-ready templates that enable data-driven decisions and deeper insights for sports analytics.
June 2025
Activity
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Quality Metrics
Correctness81.6%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
RStan
Technical Skills
Bayesian InferenceData AnalysisData VisualizationEmpirical BayesMachine LearningR ProgrammingStan ModelingStatistical Modeling
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