ryo-ngked/data-science-training-2025
Aug 2025 – Sep 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
Basic Arithmetic OperationsBoolean LogicData AnalysisData ScienceData TypesDocumentation
NgHooiKheng
PROFILE
Nghooikheng
Developed a suite of Jupyter notebooks and documentation in the ryo-ngked/data-science-training-2025 repository to support data science training and onboarding. The work included authoring Python-based materials covering arithmetic, variables, data types, and functions, as well as hands-on exercises using the Titanic dataset. Expanded the project with end-to-end workflows for data cleaning, preprocessing, and visualization, featuring practical exercises on missing value imputation, scaling, date parsing, and character encodings. Leveraged pandas and Seaborn to demonstrate reproducible data analysis and visualization techniques. All contributions emphasized clear documentation, structured progress tracking, and reusable assets to accelerate learning and improve data quality.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
4
Lines of code
38
Activity Months2
Your Network
20 peopleWork History
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025: Delivered two end-to-end notebooks in ryo-ngked/data-science-training-2025 that advance data cleaning, preprocessing, and visualization workflows. The Data Cleaning and Preprocessing Notebook Series provides practical exercises, checks, and techniques for handling missing values, scaling, date parsing, encodings, and inconsistent data entry, while the Data Visualization with Seaborn Notebook guides environment setup, loading FIFA rankings data, and plotting basic visualizations. No major bugs were reported this month; all work was committed with clear traceability. Business impact includes faster data preparation, improved data quality, and reusable training assets that shorten time-to-insight for data science initiatives. Technologies demonstrated include Python, Jupyter notebooks, pandas-based preprocessing patterns, data quality checks, and Seaborn visualizations.
6 Commits • 2 Features
Sep 1, 2025September 2025: Delivered two end-to-end notebooks in ryo-ngked/data-science-training-2025 that advance data cleaning, preprocessing, and visualization workflows. The Data Cleaning and Preprocessing Notebook Series provides practical exercises, checks, and techniques for handling missing values, scaling, date parsing, encodings, and inconsistent data entry, while the Data Visualization with Seaborn Notebook guides environment setup, loading FIFA rankings data, and plotting basic visualizations. No major bugs were reported this month; all work was committed with clear traceability. Business impact includes faster data preparation, improved data quality, and reusable training assets that shorten time-to-insight for data science initiatives. Technologies demonstrated include Python, Jupyter notebooks, pandas-based preprocessing patterns, data quality checks, and Seaborn visualizations.
September 2025
August 2025
7 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: Delivered two core materials for the data science training program: (1) Intro to Python Programming Notebooks covering arithmetic, variables, data types, and functions, including a Titanic dataset example and hands-on exercises; (2) Documentation: Progress Tracking and Learning Plan featuring a structured README progress template and guidance for next steps. No major bugs fixed this month. Business impact: accelerated learner onboarding, improved visibility into study progress, and a scalable materials baseline that supports consistent practice and future course expansion. Technical achievements and skills demonstrated: Jupyter notebook authoring, Python fundamentals pedagogy, data-science workflow concepts, Git/version-control discipline with clear commit history, and documentation best practices.
August 2025
7 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: Delivered two core materials for the data science training program: (1) Intro to Python Programming Notebooks covering arithmetic, variables, data types, and functions, including a Titanic dataset example and hands-on exercises; (2) Documentation: Progress Tracking and Learning Plan featuring a structured README progress template and guidance for next steps. No major bugs fixed this month. Business impact: accelerated learner onboarding, improved visibility into study progress, and a scalable materials baseline that supports consistent practice and future course expansion. Technical achievements and skills demonstrated: Jupyter notebook authoring, Python fundamentals pedagogy, data-science workflow concepts, Git/version-control discipline with clear commit history, and documentation best practices.
Activity
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Quality Metrics
Correctness97.0%
Maintainability97.0%
Architecture95.4%
Performance95.4%
AI Usage23.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
Basic Arithmetic OperationsBoolean LogicCharacter EncodingsData AnalysisData CleaningData PreprocessingData ScienceData TypesData VisualizationData WranglingDate ParsingDocumentationFuzzyWuzzyIntro to ProgrammingJupyter Notebook
Repositories Contributed To
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