Tada Maen worked on the tadamaen/DSA3101-Group-Project-Group-3 repository, building a modular data analysis infrastructure and scalable codebase for a group project. Over two months, Tada established a reproducible workflow using Python and Jupyter Notebooks, organizing data pipelines and project structure to support collaborative analytics on survey and historical Excel datasets. The work included consolidating and refactoring Colab notebooks, implementing data cleaning and preprocessing routines, and introducing a src directory for modular Python development. By focusing on codebase hygiene, documentation, and version control, Tada enabled faster onboarding, improved reproducibility, and a maintainable foundation for ongoing machine learning and analysis.
April 2025 monthly summary for tadamaen/DSA3101-Group-Project-Group-3: Delivered data-ready infrastructure and a cleaner codebase for the group project. Key features include Subgroup A Colab code updates consolidating Q2 and Q3 notebooks with MR-aligned changes, and data architecture enhancements with new datasets and folder structure (data/, EXTERNAL, RAW) to support reproducible analyses. Major bugs fixed involved data hygiene and repo cleanliness, including removal of deprecated/processed files and obsolete assets, and a targeted cleanup of obsolete notebooks. The overall impact is improved reproducibility, faster onboarding for new contributors, and a scalable project structure enabling modular Python development via a new src folder, plus ongoing documentation improvements. Technologies/skills demonstrated include data management, notebook and Colab standardization, Python packaging (src), version control discipline, and clear technical documentation.
April 2025 monthly summary for tadamaen/DSA3101-Group-Project-Group-3: Delivered data-ready infrastructure and a cleaner codebase for the group project. Key features include Subgroup A Colab code updates consolidating Q2 and Q3 notebooks with MR-aligned changes, and data architecture enhancements with new datasets and folder structure (data/, EXTERNAL, RAW) to support reproducible analyses. Major bugs fixed involved data hygiene and repo cleanliness, including removal of deprecated/processed files and obsolete assets, and a targeted cleanup of obsolete notebooks. The overall impact is improved reproducibility, faster onboarding for new contributors, and a scalable project structure enabling modular Python development via a new src folder, plus ongoing documentation improvements. Technologies/skills demonstrated include data management, notebook and Colab standardization, Python packaging (src), version control discipline, and clear technical documentation.
March 2025: Delivered foundational scaffolding and data workflows for tadamaen/DSA3101-Group-Project-Group-3. Established a runnable project skeleton, comprehensive onboarding documentation, and scalable data handling for USS survey data and historical Excel datasets. Built Subgroup A and Subgroup B Python codebases with initial question logic, and created Subgroup A Colab notebooks to enable reproducible experiments. Prepared Batch 3 workspace with cleanup to reduce clutter and technical debt, enabling faster future feature delivery and analytics.
March 2025: Delivered foundational scaffolding and data workflows for tadamaen/DSA3101-Group-Project-Group-3. Established a runnable project skeleton, comprehensive onboarding documentation, and scalable data handling for USS survey data and historical Excel datasets. Built Subgroup A and Subgroup B Python codebases with initial question logic, and created Subgroup A Colab notebooks to enable reproducible experiments. Prepared Batch 3 workspace with cleanup to reduce clutter and technical debt, enabling faster future feature delivery and analytics.
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