chsharrison/Sci_comp_F24
Oct 2024 – Dec 2024
3 Months active
Languages Used
Jupyter NotebookMarkdownPythonJSONSQL
Technical Skills
Data AnalysisJupyter NotebookLaTeXNumPyPandasPython Programming
tama465
PROFILE
Tama465
Israt Tama developed a suite of data science and asset management features for the chsharrison/Sci_comp_F24 repository over three months, focusing on reproducible analytics and educational tooling. She delivered Jupyter notebooks covering statistics, time series analysis, machine learning, and dynamic systems, using Python, Pandas, and scikit-learn to support hands-on coursework and streamline onboarding. Her work included a flood risk analysis notebook that integrated data cleaning, classification, and statistical testing to explore relationships in real-world datasets. By stabilizing document lifecycle management and maintaining repository hygiene, Israt ensured that learning materials and project assets remained current, organized, and accessible for contributors.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
19Total
Bugs
0
Commits
19
Features
9
Lines of code
15,154
Activity Months3
Your Network
37 people
Work History
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for chsharrison/Sci_comp_F24 highlighting end-to-end flood risk analytics delivery and final deliverables.
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for chsharrison/Sci_comp_F24 highlighting end-to-end flood risk analytics delivery and final deliverables.
December 2024
November 2024
9 Commits • 5 Features
Nov 1, 2024November 2024 delivered a cohesive set of Jupyter notebooks covering statistics, file transfer workflows, time series analysis, dynamic systems modeling, and machine learning labs. These notebooks enhance practical data science training, improve reproducibility, and streamline onboarding for new contributors. No major bugs were reported this month; maintenance focused on cleanup and clarity, including removal of an outdated Lab13.2 notebook to reduce confusion and drift in the repository.
November 2024
9 Commits • 5 Features
Nov 1, 2024November 2024 delivered a cohesive set of Jupyter notebooks covering statistics, file transfer workflows, time series analysis, dynamic systems modeling, and machine learning labs. These notebooks enhance practical data science training, improve reproducibility, and streamline onboarding for new contributors. No major bugs were reported this month; maintenance focused on cleanup and clarity, including removal of an outdated Lab13.2 notebook to reduce confusion and drift in the repository.
October 2024
7 Commits • 2 Features
Oct 1, 20242024-10 Monthly Summary for chsharrison/Sci_comp_F24: Delivered two core features that strengthen asset management and learning tooling. Implemented the Israt_Tama project assets/documents lifecycle, enabling add/delete/re-add transitions for Final_Proposal_Israt.pdf to ensure asset integrity; and shipped educational notebooks for Zotero/LaTeX Lab 9.1 and Data Science Lab as ready-to-run Jupyter notebooks to support coursework and hands-on exercises. No high-severity bugs reported; lifecycle transitions were stabilized to prevent asset drift. Overall impact: reduces risk from outdated documents and accelerates learning workflows. Technologies demonstrated: Git-based asset lifecycle management, Jupyter notebooks, LaTeX/Zotero workflow integration, and data science tooling.
7 Commits • 2 Features
Oct 1, 20242024-10 Monthly Summary for chsharrison/Sci_comp_F24: Delivered two core features that strengthen asset management and learning tooling. Implemented the Israt_Tama project assets/documents lifecycle, enabling add/delete/re-add transitions for Final_Proposal_Israt.pdf to ensure asset integrity; and shipped educational notebooks for Zotero/LaTeX Lab 9.1 and Data Science Lab as ready-to-run Jupyter notebooks to support coursework and hands-on exercises. No high-severity bugs reported; lifecycle transitions were stabilized to prevent asset drift. Overall impact: reduces risk from outdated documents and accelerates learning workflows. Technologies demonstrated: Git-based asset lifecycle management, Jupyter notebooks, LaTeX/Zotero workflow integration, and data science tooling.
October 2024
Activity
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Quality Metrics
Correctness89.4%
Maintainability89.4%
Architecture87.4%
Performance86.2%
AI Usage23.2%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Data AnalysisData CleaningData ManipulationData VisualizationDataCampDifferential EquationsFile TransferHPC UsageJupyter NotebookJupyter NotebooksLaTeXMachine LearningMatplotlibNumPyNumerical Methods
Repositories Contributed To
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