d2cml-ai/CausalAI-Course
Sep 2025 – Sep 2025
1 Month active
Languages Used
CSVJuliaPythonR
Technical Skills
Causal InferenceData AnalysisData EngineeringMachine LearningStatistical Modeling
valeriahoyosm
PROFILE
Valeriahoyosm
Contributed to the d2cml-ai/CausalAI-Course repository by integrating a new Districtwise Literacy Rates Data Resource, which provides structured demographic and educational statistics from the 2011 Census to support downstream analytics and feature development. Leveraged data engineering and statistical modeling skills to introduce the dataset in CSV format, ensuring clear field names and descriptions for reproducibility and future causal inference work. Addressed technical debt by removing legacy analysis scripts written in R, Julia, and Python, streamlining the codebase and improving repository hygiene. These efforts enhanced data governance, clarified provenance, and facilitated onboarding and collaboration for ongoing machine learning projects.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
753
Activity Months1
Your Network
24 peopleWork History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025: Key data-source integration and repository hygiene improvements for d2cml-ai/CausalAI-Course. Delivered a new Districtwise Literacy Rates Data Resource (Districtwise_literacy_rates.csv) with field names and descriptions from the 2011 Census to enable downstream analytics and feature development. Performed deprecation cleanup by removing literacy rate analysis scripts (R, Julia, Python), reducing technical debt and maintenance risk. These efforts strengthen data provenance, reproducibility, and readiness for causal modeling, while improving onboarding and collaboration across the team.
2 Commits • 1 Features
Sep 1, 2025September 2025: Key data-source integration and repository hygiene improvements for d2cml-ai/CausalAI-Course. Delivered a new Districtwise Literacy Rates Data Resource (Districtwise_literacy_rates.csv) with field names and descriptions from the 2011 Census to enable downstream analytics and feature development. Performed deprecation cleanup by removing literacy rate analysis scripts (R, Julia, Python), reducing technical debt and maintenance risk. These efforts strengthen data provenance, reproducibility, and readiness for causal modeling, while improving onboarding and collaboration across the team.
September 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability90.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJuliaPythonR
Technical Skills
Causal InferenceData AnalysisData EngineeringMachine LearningStatistical Modeling
Repositories Contributed To
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