racousin/data_science_practice_2024
Nov 2024 – Jan 2025
2 Months active
Languages Used
CSSCSVHTMLJupyter NotebookPython
Technical Skills
API IntegrationBeautifulSoupData AnalysisData CollectionData EntryData Preprocessing
HuangYuling68
PROFILE
Huangyuling68
During a two-month period, Yuling Huang developed and enhanced data science learning assets in the racousin/data_science_practice_2024 repository. She created reusable Jupyter Notebooks and Python modules that guide users through end-to-end workflows, including data collection, preprocessing, and baseline machine learning with scikit-learn and XGBoost. Her work included a new arithmetic utilities package and a time series analysis notebook that addressed data inconsistencies and missing values, improving reproducibility and hands-on experience for students. By focusing on practical tooling and clear data pipelines, Yuling enabled faster iteration of course materials and provided learners with robust, real-world data science practice environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
12,322
Activity Months2
Your Network
15 people
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025Monthly summary for 2025-01 for racousin/data_science_practice_2024: Key feature delivered: Module 5 Data Collection and Time Series Analysis Notebook (exercise1.ipynb) introducing end-to-end data collection, time series plotting, data preprocessing (handling inconsistencies and missing values), and setup for time series analysis and ML tasks. Major bugs fixed: none reported this month. Overall impact and accomplishments: Enhanced the module 5 hands-on experience by providing a practical, end-to-end notebook that accelerates data workflows and improves reproducibility; prepared students for real-world TS/ML tasks. Technologies/skills demonstrated: Python, Jupyter Notebooks, data preprocessing, time series analysis, data visualization, and git/version control.
1 Commits • 1 Features
Jan 1, 2025Monthly summary for 2025-01 for racousin/data_science_practice_2024: Key feature delivered: Module 5 Data Collection and Time Series Analysis Notebook (exercise1.ipynb) introducing end-to-end data collection, time series plotting, data preprocessing (handling inconsistencies and missing values), and setup for time series analysis and ML tasks. Major bugs fixed: none reported this month. Overall impact and accomplishments: Enhanced the module 5 hands-on experience by providing a practical, end-to-end notebook that accelerates data workflows and improves reproducibility; prepared students for real-world TS/ML tasks. Technologies/skills demonstrated: Python, Jupyter Notebooks, data preprocessing, time series analysis, data visualization, and git/version control.
January 2025
November 2024
4 Commits • 3 Features
Nov 1, 2024Month: 2024-11 — Focused on delivering practical, reusable data science learning assets and lightweight tooling. Key features delivered include enhancements to practice exercises, a new arithmetic utilities module, and an end-to-end DS workflow with a new submission dataset. No major bugs reported; updates include traceable commits in racousin/data_science_practice_2024. Overall impact: improved learner experience, faster course material iteration, and reusable tooling, enabling students to practice end-to-end workflows and submit results. Technologies demonstrated: Python packaging, module development, data collection and preprocessing pipelines, basic ML modeling (linear regression), and dataset generation.
November 2024
4 Commits • 3 Features
Nov 1, 2024Month: 2024-11 — Focused on delivering practical, reusable data science learning assets and lightweight tooling. Key features delivered include enhancements to practice exercises, a new arithmetic utilities module, and an end-to-end DS workflow with a new submission dataset. No major bugs reported; updates include traceable commits in racousin/data_science_practice_2024. Overall impact: improved learner experience, faster course material iteration, and reusable tooling, enabling students to practice end-to-end workflows and submit results. Technologies demonstrated: Python packaging, module development, data collection and preprocessing pipelines, basic ML modeling (linear regression), and dataset generation.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance76.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
CSSCSVHTMLJupyter NotebookPython
Technical Skills
API IntegrationBeautifulSoupData AnalysisData CollectionData EntryData PreprocessingData ScienceJupyter NotebookMachine LearningMatplotlibNumPyPackage DevelopmentPandasPythonRequests
Repositories Contributed To
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