tadamaen/DSA3101-Group-Project-Group-3
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVPythonJupyter Notebook
Technical Skills
Agent-Based ModelingData AnalysisData CleaningData CollectionData EngineeringJupyter Notebook
wwwanting
PROFILE
Wwwanting
Developed a Python-based Theme Park Wait Time Analysis Toolkit in the tadamaen/DSA3101-Group-Project-Group-3 repository, enabling simulation of visitor flow, attraction capacities, and ride durations with ready-to-analyze datasets and visualizations. Applied agent-based modeling, data engineering, and data cleaning to streamline operational analysis and improve data hygiene. Enhanced optimization analytics by implementing functions to calculate and visualize wait-time reductions, supporting scenario comparison and decision-making. Updated and maintained Jupyter notebooks for Burrows-Wheeler Transform optimization, improving reproducibility and collaboration. Utilized Python, Pandas, and Matplotlib throughout, focusing on maintainable code, artifact management, and clear data structures to support ongoing analysis.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
3
Lines of code
34,880
Activity Months2
Your Network
11 peopleShared Repositories
11
Work History
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for tadamaen/DSA3101-Group-Project-Group-3. Delivered two primary features focused on optimization analytics and visualization. Improved research artifacts with updated notebooks and visualizations to support analysis and presentation of optimization strategies.
4 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for tadamaen/DSA3101-Group-Project-Group-3. Delivered two primary features focused on optimization analytics and visualization. Improved research artifacts with updated notebooks and visualizations to support analysis and presentation of optimization strategies.
April 2025
March 2025
7 Commits • 1 Features
Mar 1, 2025March 2025 - Summary for tadamaen/DSA3101-Group-Project-Group-3: Key features delivered: - Theme Park Wait Time Analysis Toolkit: a Python-based toolkit for simulating visitor flow, modeling attraction capacities and ride durations, and generating visualizations to evaluate operational changes and guest satisfaction. Includes a data structure for wait-time data and ready-to-analyze datasets for analysis and visualization. Major bugs fixed: - Notebook Data Artifacts Cleanup and Naming Consistency: automated notebook upload with base64-encoded data and renaming to improve naming consistency (no functional code changes). - Obsolete Wait Time Dataset Cleanup: removal of an obsolete raw wait time dataset to reduce clutter and improve repository hygiene. Overall impact and accomplishments: - Enables data-driven evaluation of park operations, improves data hygiene and artifact management, and streamlines data pipelines for faster analyses. Technologies/skills demonstrated: - Python analytics and data modeling, data visualization, dataset management, automation of artifact hygiene, and consistent naming conventions.
March 2025
7 Commits • 1 Features
Mar 1, 2025March 2025 - Summary for tadamaen/DSA3101-Group-Project-Group-3: Key features delivered: - Theme Park Wait Time Analysis Toolkit: a Python-based toolkit for simulating visitor flow, modeling attraction capacities and ride durations, and generating visualizations to evaluate operational changes and guest satisfaction. Includes a data structure for wait-time data and ready-to-analyze datasets for analysis and visualization. Major bugs fixed: - Notebook Data Artifacts Cleanup and Naming Consistency: automated notebook upload with base64-encoded data and renaming to improve naming consistency (no functional code changes). - Obsolete Wait Time Dataset Cleanup: removal of an obsolete raw wait time dataset to reduce clutter and improve repository hygiene. Overall impact and accomplishments: - Enables data-driven evaluation of park operations, improves data hygiene and artifact management, and streamlines data pipelines for faster analyses. Technologies/skills demonstrated: - Python analytics and data modeling, data visualization, dataset management, automation of artifact hygiene, and consistent naming conventions.
Activity
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Quality Metrics
Correctness83.6%
Maintainability83.6%
Architecture83.6%
Performance83.6%
AI Usage23.6%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPython
Technical Skills
Agent-Based ModelingAlgorithm OptimizationComputational AnalysisData AnalysisData CleaningData CollectionData EngineeringData StructuresData VisualizationJupyter NotebookMachine LearningMatplotlibMesaPandasPython
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