datakind/student-success-tool
Dec 2024 – Feb 2025
3 Months active
Languages Used
PythonCSV
Technical Skills
Data GenerationScriptingTestingBackend DevelopmentCode FormattingCode Refactoring
Zak Miller
PROFILE
Zak Miller
Zak Miller enhanced the datakind/student-success-tool repository by developing and refining synthetic data generation pipelines using Python and Pandas. Over three months, Zak focused on backend development and data engineering, implementing robust institution ID handling, unique student GUID assignment, and accurate year formatting to align with schema requirements. He addressed data integrity issues by enforcing single institution IDs and correcting leap-year logic, while also improving boolean casting and test reliability. Zak’s work emphasized code quality through refactoring and expanded unit testing, resulting in more reliable, schema-compliant datasets that support analytics and demos with reduced risk of downstream data quality issues.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
10Total
Bugs
4
Commits
10
Features
2
Lines of code
428
Activity Months3
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15 peopleShared Repositories
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Work History
February 2025
2 Commits
Feb 1, 2025February 2025: Hardened synthetic data generation for the datakind/student-success-tool to improve data integrity and reliability for analytics and demos. Implemented upfront generation of unique student GUIDs using Faker's unique method to prevent duplicates and schema violations, and corrected leap-year handling to ensure cohort year ranges are represented as 'YYYY-YY'. These changes reduce downstream data quality risks and enhance confidence in synthetic datasets.
2 Commits
Feb 1, 2025February 2025: Hardened synthetic data generation for the datakind/student-success-tool to improve data integrity and reliability for analytics and demos. Implemented upfront generation of unique student GUIDs using Faker's unique method to prevent duplicates and schema violations, and corrected leap-year handling to ensure cohort year ranges are represented as 'YYYY-YY'. These changes reduce downstream data quality risks and enhance confidence in synthetic datasets.
February 2025
January 2025
7 Commits • 2 Features
Jan 1, 2025Performance-focused monthly summary for datakind/student-success-tool (Jan 2025). Delivered robust synthetic data generation features and stability improvements, with a focus on business value and data reliability. Key items include flexible Institution ID handling, year-format updates, and robust boolean casting, all supported by expanded tests and code-quality improvements. Result: higher reliability of synthetic datasets, fewer edge-case failures in downstream analytics, and faster development cycles with increased test confidence.
January 2025
7 Commits • 2 Features
Jan 1, 2025Performance-focused monthly summary for datakind/student-success-tool (Jan 2025). Delivered robust synthetic data generation features and stability improvements, with a focus on business value and data reliability. Key items include flexible Institution ID handling, year-format updates, and robust boolean casting, all supported by expanded tests and code-quality improvements. Result: higher reliability of synthetic datasets, fewer edge-case failures in downstream analytics, and faster development cycles with increased test confidence.
December 2024
1 Commits
Dec 1, 2024December 2024 monthly summary for datakind/student-success-tool: focused on stabilizing the synthetic data generator to improve data consistency and analytic reliability. Delivered a bug fix enforcing a single institution ID per dataset and corrected a test file copy-paste error, enhancing test accuracy and data integrity across analytics pipelines.
1 Commits
Dec 1, 2024December 2024 monthly summary for datakind/student-success-tool: focused on stabilizing the synthetic data generator to improve data consistency and analytic reliability. Delivered a bug fix enforcing a single institution ID per dataset and corrected a test file copy-paste error, enhancing test accuracy and data integrity across analytics pipelines.
December 2024
Activity
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Quality Metrics
Correctness85.0%
Maintainability82.0%
Architecture76.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPython
Technical Skills
Backend DevelopmentCode FormattingCode RefactoringData EngineeringData GenerationData TransformationData ValidationDate ManipulationPandasPython ScriptingScriptingSynthetic DataTestingdata generationdata validation
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