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
Avish Maniar 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. Using Python in a Jupyter Notebook environment, Avish leveraged pandas, seaborn, and matplotlib to create a reproducible workflow that demonstrates Pearson correlation and generates a visual correlation matrix. This contribution addressed the need for practical, hands-on tools in data science education, enabling learners to better understand variable relationships. The work was delivered without introducing defects, maintaining repository stability, and provided a clear, educational resource that enhances onboarding and practical learning for new data science practitioners.
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 people
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
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