Teradata/jupyter-demos
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonSQLJSONJupyter NotebookMarkdown
Technical Skills
Data ScienceDatabase ManagementJupyter NotebookMachine LearningMachine Learning OperationsModelOps
JasonD13
PROFILE
Jasond13
Jason Dsouza developed and enhanced data science workflows in the Teradata/jupyter-demos repository, focusing on end-to-end machine learning use cases such as Telco churn analysis and Parkinson’s disease prediction. He integrated Enterprise Feature Store (EFS) and Vantage connectivity to streamline feature discovery, dataset cataloging, and reproducible experiments. Using Python, SQL, and Jupyter Notebooks, Jason improved notebook reliability by addressing environment setup issues, refining feature engineering, and implementing safeguards against accidental data loss. His work emphasized clear documentation and logical notebook structure, enabling maintainable, business-focused analytics while ensuring robust model training, evaluation, and safe, reproducible execution for future users.
Overall Statistics
Feature vs Bugs
44%Features
Repository Contributions
10Total
Bugs
5
Commits
10
Features
4
Lines of code
36,096
Activity Months2
Your Network
18 people
Work History
October 2025
7 Commits • 4 Features
Oct 1, 2025October 2025 performance summary for Teradata/jupyter-demos. Delivered end-to-end Telco churn and Parkinson's disease prediction demos with Enterprise Feature Store (EFS) integration and robust data/ML workflows. Focused on business value through actionable insights, reproducible experiments, and clearer documentation.
7 Commits • 4 Features
Oct 1, 2025October 2025 performance summary for Teradata/jupyter-demos. Delivered end-to-end Telco churn and Parkinson's disease prediction demos with Enterprise Feature Store (EFS) integration and robust data/ML workflows. Focused on business value through actionable insights, reproducible experiments, and clearer documentation.
October 2025
September 2025
3 Commits
Sep 1, 2025Monthly work summary for 2025-09 focused on stabilizing Teradata/jupyter-demos with critical bug fixes that improve notebook reliability and safety. Delivered fixes ensure H2O libraries load correctly in notebook environments and added safeguards to prevent accidental data loss during testing.
September 2025
3 Commits
Sep 1, 2025Monthly work summary for 2025-09 focused on stabilizing Teradata/jupyter-demos with critical bug fixes that improve notebook reliability and safety. Delivered fixes ensure H2O libraries load correctly in notebook environments and added safeguards to prevent accidental data loss during testing.
Activity
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Quality Metrics
Correctness86.0%
Maintainability86.0%
Architecture80.0%
Performance82.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Customer Churn AnalysisData AnalysisData CleaningData ExplorationData ScienceData VisualizationDatabase ManagementDocumentationEnterprise Feature Store (EFS)Feature EngineeringJupyter NotebookJupyter NotebooksMachine LearningMachine Learning OperationsModelOps
Repositories Contributed To
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