Teradata/jupyter-demos
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonSQLJSONJupyter NotebookMarkdown
Technical Skills
Data ScienceDatabase ManagementJupyter NotebookMachine LearningMachine Learning OperationsModelOps
Jason Dsouza
PROFILE
Jason Dsouza
Contributed to the Teradata/jupyter-demos repository by developing end-to-end data science solutions, including customer churn analysis and Parkinson’s disease prediction workflows. Leveraged Python, SQL, and Jupyter Notebooks to integrate Enterprise Feature Store (EFS) capabilities, enabling robust feature discovery and reproducible machine learning pipelines. Enhanced notebook reliability by resolving environment configuration issues and implementing safeguards against accidental data loss. Improved documentation and notebook structure to support clearer data preparation, model training, and evaluation processes. Addressed multiple bugs to ensure stable, clean demo runs and maintainable code. The work emphasized actionable insights, reproducibility, and seamless integration with Teradata Vantage and VantageCloud Lake.
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
20 peopleShared Repositories
20
Ashwin-apMember
chetan-hiraparaMember
dallasbowdenMember
Daniel HerreraMember
db255058Member
Mohd-HassanMember
Jason DsouzaMember
JanethLMember
Nitin SreeramMember
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
Loading activity data...
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
Overview of all repositories you've contributed to across your timeline