mauricioantelis/TC1002S
Mar 2025 – Mar 2025
1 Month active
Languages Used
CSVJupyter NotebookPythonText
Technical Skills
Data AnalysisData VisualizationDocumentationJupyter NotebookMachine LearningMatplotlib
DanteGuzmánR
PROFILE
Danteguzmánr
Dante Guzman developed two educational assets for the mauricioantelis/TC1002S repository, focusing on improving learner onboarding and hands-on machine learning practice. He created Jupyter notebooks for activity setup and data exploration, covering the cartwheel, iris, and digits datasets with CSV data and visualizations. His approach emphasized reproducibility and auditability, with explicit commits and comprehensive documentation. Dante also expanded machine learning coverage by implementing classification and clustering experiments on the Iris dataset using Python, Pandas, and Scikit-learn. The work demonstrated depth in data analysis and visualization, providing clear, evaluable activities that support knowledge transfer and reproducible experimentation for students.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
5,039
Activity Months1
Your Network
27 people
Work History
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025: Delivered two key educational assets for mauricioantelis/TC1002S that enhance learner onboarding, reproducibility, and hands-on ML practice. Implemented Educational Activity Setup and Data Exploration Notebooks for cartwheel, iris, and digits datasets with CSV data and visuals, accompanied by an evaluable activity readme. Expanded ML coverage with Iris Notebooks for classification and clustering experiments. All work is traceable through explicit commits, supporting auditability and knowledge transfer. Technologies demonstrated include Python data science stack, Jupyter notebooks, data exploration, data visualization, and ML experimentation.
4 Commits • 2 Features
Mar 1, 2025March 2025: Delivered two key educational assets for mauricioantelis/TC1002S that enhance learner onboarding, reproducibility, and hands-on ML practice. Implemented Educational Activity Setup and Data Exploration Notebooks for cartwheel, iris, and digits datasets with CSV data and visuals, accompanied by an evaluable activity readme. Expanded ML coverage with Iris Notebooks for classification and clustering experiments. All work is traceable through explicit commits, supporting auditability and knowledge transfer. Technologies demonstrated include Python data science stack, Jupyter notebooks, data exploration, data visualization, and ML experimentation.
March 2025
Activity
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Quality Metrics
Correctness87.4%
Maintainability85.0%
Architecture85.0%
Performance85.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPythonText
Technical Skills
Data AnalysisData VisualizationDocumentationJupyter NotebookMachine LearningMatplotlibNumpyPandasPythonScikit-learnSeaborn
Repositories Contributed To
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