amosproj/amos2024ws01-rtdip-data-quality-checker
Nov 2024 – Feb 2025
4 Months active
Languages Used
MDPDFPNGPythonTSVSQL
Technical Skills
Code FormattingCode OrganizationData EngineeringData QualityData Quality TestingData Science
Minh Khue Tran
PROFILE
Minh Khue Tran
Minh Khue Tran developed and enhanced data quality and machine learning features for the amosproj/amos2024ws01-rtdip-data-quality-checker repository over four months. She implemented robust PySpark modules for linear regression, anomaly detection, and K-Nearest Neighbors forecasting, focusing on maintainable code organization and comprehensive test coverage. Her work included one-hot encoding for Spark DataFrames, improved error handling in anomaly detection, and detailed documentation with runnable examples to streamline onboarding. Using Python and SQL, she prioritized data validation, model evaluation, and pipeline reliability, delivering features that improved large-scale data quality workflows and enabled more consistent, scalable integration into business data pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
30Total
Bugs
0
Commits
30
Features
8
Lines of code
1,924
Activity Months4
Your Network
28 people
Work History
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for amosproj/amos2024ws01-rtdip-data-quality-checker: delivered documentation enhancements for the KNN component and Python SDK pipeline to improve usability and onboarding. No functional changes to KNN; documentation now includes runnable examples and clarified SparkSession/DataFrame usage, enabling faster adoption and more consistent integration into data quality workflows.
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for amosproj/amos2024ws01-rtdip-data-quality-checker: delivered documentation enhancements for the KNN component and Python SDK pipeline to improve usability and onboarding. No functional changes to KNN; documentation now includes runnable examples and clarified SparkSession/DataFrame usage, enabling faster adoption and more consistent integration into data quality workflows.
February 2025
January 2025
8 Commits • 2 Features
Jan 1, 2025Month: 2025-01 — Highlights include delivering a KNN Forecasting Module and its tests for the RTDIP SDK, plus Sprint 12 deliverables documentation. Key improvements: implemented PySpark-based KNearestNeighbors to support time-series predictions with temporal weighting and multiple distance metrics; added training, prediction, and robustness unit tests; fixed import errors and restructured the forecasting package to improve maintainability (renaming machine_learning to forecasting). Also completed sprint planning materials and backlog organization for Sprint 12 to enhance visibility and planning accuracy.
January 2025
8 Commits • 2 Features
Jan 1, 2025Month: 2025-01 — Highlights include delivering a KNN Forecasting Module and its tests for the RTDIP SDK, plus Sprint 12 deliverables documentation. Key improvements: implemented PySpark-based KNearestNeighbors to support time-series predictions with temporal weighting and multiple distance metrics; added training, prediction, and robustness unit tests; fixed import errors and restructured the forecasting package to improve maintainability (renaming machine_learning to forecasting). Also completed sprint planning materials and backlog organization for Sprint 12 to enhance visibility and planning accuracy.
December 2024
4 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for amosproj/amos2024ws01-rtdip-data-quality-checker. Focused on strengthening data quality and ML pipeline reliability in the Spark-based workflow. Delivered clearer error messaging in anomaly detection, fortified validation and test coverage for Linear Regression with large datasets, improving observability, stability, and scalability with tangible business value.
4 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for amosproj/amos2024ws01-rtdip-data-quality-checker. Focused on strengthening data quality and ML pipeline reliability in the Spark-based workflow. Delivered clearer error messaging in anomaly detection, fortified validation and test coverage for Linear Regression with large datasets, improving observability, stability, and scalability with tangible business value.
December 2024
November 2024
16 Commits • 3 Features
Nov 1, 2024Monthly summary for 2024-11: amos2024ws01-rtdip-data-quality-checker. Focused on delivering production-ready data quality and ML support in the data quality checker, with emphasis on robust model evaluation, Spark ML utilities, and maintainability. Highlights cover feature delivery, bug/quality fixes, and overall impact for the business and engineering teams.
November 2024
16 Commits • 3 Features
Nov 1, 2024Monthly summary for 2024-11: amos2024ws01-rtdip-data-quality-checker. Focused on delivering production-ready data quality and ML support in the data quality checker, with emphasis on robust model evaluation, Spark ML utilities, and maintainability. Highlights cover feature delivery, bug/quality fixes, and overall impact for the business and engineering teams.
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.6%
Architecture89.4%
Performance86.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MDPDFPNGPythonSQLTSV
Technical Skills
Anomaly DetectionCode FormattingCode LintingCode OrganizationData EngineeringData QualityData Quality TestingData ScienceData TransformationData ValidationData WranglingDependency ManagementDocumentationETLML Pipelines
Repositories Contributed To
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