ryo-ngked/data-science-training-2025
Aug 2025 – Sep 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPythonSQL
Technical Skills
Boolean AlgebraConditional LogicConditional StatementsData AnalysisData ScienceData Science Education
Nuratiqahahmad
PROFILE
Nuratiqahahmad
Nur Atiqah Ahmad developed a modular suite of Jupyter notebook-based training materials for the ryo-ngked/data-science-training-2025 repository, focusing on foundational Python programming and practical data science workflows. She designed hands-on exercises covering arithmetic, functions, conditional logic, and data structures, then expanded into data cleaning, preprocessing, and visualization using pandas, seaborn, and matplotlib. Her approach emphasized reproducible, end-to-end workflows, enabling learners to progress from basic programming to data wrangling and storytelling. Weekly progress logs and updated documentation improved learner guidance. The work demonstrated depth in both technical content and instructional design, supporting scalable, self-paced learning for entry-level data science.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
26Total
Bugs
0
Commits
26
Features
9
Lines of code
162.23
Activity Months2
Your Network
20 people
Work History
September 2025
8 Commits • 2 Features
Sep 1, 2025Summary for 2025-09: Delivered two notebook-based training modules in ryo-ngked/data-science-training-2025 that advance data preparation and data storytelling capabilities. Key features include Data Cleaning/Preprocessing/Quality Assurance Notebooks and Data Visualization Practice Notebooks. Implemented comprehensive data wrangling tasks (missing values, scaling, normalization, inconsistent data correction, encoding, date parsing) and visualization exercises with seaborn/matplotlib (bar charts and heatmaps). Total commits across both features: 8 (all "Add files via upload"), reflecting rapid content population. No major bugs fixed this month; content population and maintenance completed to readiness. Impact: provides learners with reproducible, end-to-end data preparation and visualization workflows, accelerating time-to-insight and improving training outcomes. Technologies/skills demonstrated: Python, Jupyter notebooks, pandas-based data wrangling, seaborn/matplotlib visualizations, notebook-based tutorials, and Git-based collaboration.
8 Commits • 2 Features
Sep 1, 2025Summary for 2025-09: Delivered two notebook-based training modules in ryo-ngked/data-science-training-2025 that advance data preparation and data storytelling capabilities. Key features include Data Cleaning/Preprocessing/Quality Assurance Notebooks and Data Visualization Practice Notebooks. Implemented comprehensive data wrangling tasks (missing values, scaling, normalization, inconsistent data correction, encoding, date parsing) and visualization exercises with seaborn/matplotlib (bar charts and heatmaps). Total commits across both features: 8 (all "Add files via upload"), reflecting rapid content population. No major bugs fixed this month; content population and maintenance completed to readiness. Impact: provides learners with reproducible, end-to-end data preparation and visualization workflows, accelerating time-to-insight and improving training outcomes. Technologies/skills demonstrated: Python, Jupyter notebooks, pandas-based data wrangling, seaborn/matplotlib visualizations, notebook-based tutorials, and Git-based collaboration.
September 2025
August 2025
18 Commits • 7 Features
Aug 1, 2025August 2025 monthly summary for the ryo-ngked/data-science-training-2025 repository. Delivered a modular suite of introductory Python and data science practice notebooks, expanding hands-on learning opportunities aligned with the Atiqah Kaggle course. Implemented core topics (arithmetic and variables, functions, conditional logic, lists/loops, strings/dictionaries) plus Pandas DataFrame creation and basic visualization. Added practical exercises (house cost, painting cost, Blackjack scenario) to reinforce function usage and control flow. Maintained content quality through targeted cleanup, including removal of deprecated notebook content. Enhanced learner experience and guidance with weekly progress logs and improved documentation. The work supports scalable, self-paced learning with measurable skill progression, reducing time-to-competency for entry-level data science topics.
August 2025
18 Commits • 7 Features
Aug 1, 2025August 2025 monthly summary for the ryo-ngked/data-science-training-2025 repository. Delivered a modular suite of introductory Python and data science practice notebooks, expanding hands-on learning opportunities aligned with the Atiqah Kaggle course. Implemented core topics (arithmetic and variables, functions, conditional logic, lists/loops, strings/dictionaries) plus Pandas DataFrame creation and basic visualization. Added practical exercises (house cost, painting cost, Blackjack scenario) to reinforce function usage and control flow. Maintained content quality through targeted cleanup, including removal of deprecated notebook content. Enhanced learner experience and guidance with weekly progress logs and improved documentation. The work supports scalable, self-paced learning with measurable skill progression, reducing time-to-competency for entry-level data science topics.
Activity
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Quality Metrics
Correctness99.2%
Maintainability99.2%
Architecture98.4%
Performance98.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonSQL
Technical Skills
Boolean AlgebraCharacter EncodingsConditional LogicConditional StatementsData AnalysisData CleaningData PreprocessingData ScienceData Science EducationData VisualizationDocumentationFile ManagementFunction DefinitionFuzzyWuzzyIntro to Programming
Repositories Contributed To
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