microsoft/Data-Science-For-Beginners
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData VisualizationFeature EngineeringMatplotlibPandasPearson Correlation
AvishManiar21
PROFILE
Avishmaniar21
Developed a Pearson Correlation Analysis Notebook for the microsoft/Data-Science-For-Beginners repository, focusing on visualizing linear relationships to support feature engineering and data preprocessing. Leveraged Python in a Jupyter Notebook environment, utilizing pandas for data manipulation, seaborn for visual correlation matrices, and matplotlib for enhanced data visualization. The notebook enables users to explore and interpret Pearson correlation coefficients, clarifying relationships between variables for educational and practical purposes. This contribution improved reproducibility and onboarding for new learners, aligning with the repository’s educational objectives. No major defects were reported, and the repository remained stable, reflecting a focused and well-executed engineering effort.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
87
Activity Months1
Your Network
9 peopleShared Repositories
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Anthony BartoloMember
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KanitMember
Lee StottMember
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Rory PreddyMember
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Amber WangMember
Work History
May 2025
1 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05: Delivered a new Pearson Correlation Analysis Notebook in microsoft/Data-Science-For-Beginners to visualize linear relationships, support feature engineering, and streamline preprocessing. No major defects reported; repository remained stable and aligned with educational goals. This work enhances data science learning throughput and provides learners with practical, reproducible tooling for correlation analysis.
1 Commits • 1 Features
May 1, 2025Monthly summary for 2025-05: Delivered a new Pearson Correlation Analysis Notebook in microsoft/Data-Science-For-Beginners to visualize linear relationships, support feature engineering, and streamline preprocessing. No major defects reported; repository remained stable and aligned with educational goals. This work enhances data science learning throughput and provides learners with practical, reproducible tooling for correlation analysis.
May 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData VisualizationFeature EngineeringMatplotlibPandasPearson CorrelationSeaborn
Repositories Contributed To
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