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
Wanting Beh developed a Theme Park Wait Time Analysis Toolkit in the tadamaen/DSA3101-Group-Project-Group-3 repository, enabling simulation of visitor flow and analysis of operational changes using Python, Pandas, and Matplotlib. She implemented agent-based modeling to evaluate attraction capacities and ride durations, providing ready-to-analyze datasets and visualizations for data-driven decision support. Her work included optimizing wait-time reduction analytics, building comparative bar charts, and updating Burrows-Wheeler Transform optimization notebooks to enhance scenario analysis. She also improved data hygiene by automating artifact management and cleaning obsolete datasets, demonstrating depth in data engineering, computational analysis, and reproducible research artifact development.
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
9 people
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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline