ryo-ngked/data-science-training-2025
Aug 2025 – Aug 2025
1 Month active
Languages Used
GitattributesMarkdownPythonShellYAML
Technical Skills
API IntegrationCI/CDConfigurationData ScienceData VisualizationDocumentation
ryo-ngked
PROFILE
Ryo-ngked
Worked on the ryo-ngked/data-science-training-2025 repository, delivering foundational project scaffolding and onboarding readiness through the setup of Jupyter notebooks and standardized documentation. Established a robust documentation lifecycle by migrating and consolidating content into log.md and README.md files, ensuring consistent knowledge transfer. Automated monthly progress reporting with a Python-based chart generator, configured via YAML, to streamline stakeholder updates. Improved repository hygiene by removing obsolete files and aligning configuration scripts for maintainability. Leveraged skills in Python scripting, GitHub Actions, and data visualization to support collaboration and reduce maintenance overhead, resulting in a more reliable and accessible data science training environment.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
47Total
Bugs
1
Commits
47
Features
8
Lines of code
927
Activity Months1
Your Network
20 peopleWork History
August 2025
47 Commits • 8 Features
Aug 1, 2025August 2025 monthly summary for ryo-ngked/data-science-training-2025. Delivered foundational project scaffolding and onboarding readiness, established a robust documentation lifecycle, automated progress reporting, and improved repository hygiene. These efforts unlocked faster onboarding, clearer knowledge transfer, more reliable stakeholder reporting, and reduced maintenance burden across the data-science-training initiative.
47 Commits • 8 Features
Aug 1, 2025August 2025 monthly summary for ryo-ngked/data-science-training-2025. Delivered foundational project scaffolding and onboarding readiness, established a robust documentation lifecycle, automated progress reporting, and improved repository hygiene. These efforts unlocked faster onboarding, clearer knowledge transfer, more reliable stakeholder reporting, and reduced maintenance burden across the data-science-training initiative.
August 2025
Activity
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Quality Metrics
Correctness95.4%
Maintainability95.8%
Architecture93.6%
Performance95.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
GitattributesMarkdownPythonShellYAML
Technical Skills
API IntegrationCI/CDConfigurationData ScienceData VisualizationDocumentationEnvironment VariablesFile System OperationsGitGitHub ActionsIntro to ProgrammingIntroductory ProgrammingJupyter NotebooksMatplotlibPersonal Knowledge Management
Repositories Contributed To
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