Developed end-to-end data analysis and retrieval workflows in the d2cml-ai/Data-Science-Python repository, focusing on financial, social, and healthcare datasets. Built data ingestion pipelines and interactive visualizations using Python, Pandas, and Folium, enabling users to analyze stock trends, construct equal-weighted portfolios, and explore hospital locations on dynamic maps. Integrated APIs such as Yahoo Finance and Reddit for comprehensive data collection, and managed dependencies and documentation to streamline onboarding. Delivered a Retrieval Augmented Generation notebook tutorial leveraging ChromaDB and OpenAI embeddings, demonstrating persistent vector storage and reproducible LLM experiments. All work emphasized reproducibility, modularity, and practical application for data science workflows.
October 2025 monthly summary focusing on key accomplishments in the Data-Science-Python repo. Delivered an end-to-end Retrieval Augmented Generation (RAG) notebook tutorial using ChromaDB and OpenAI embeddings, enabling reproducible LLM experiments and enhanced document retrieval workflows.
October 2025 monthly summary focusing on key accomplishments in the Data-Science-Python repo. Delivered an end-to-end Retrieval Augmented Generation (RAG) notebook tutorial using ChromaDB and OpenAI embeddings, enabling reproducible LLM experiments and enhanced document retrieval workflows.
September 2025 monthly summary for d2cml-ai/Data-Science-Python: Delivered foundational data science capabilities enabling end-to-end financial and social data analysis, and established project scaffolding for Practice 1. Implemented data ingestion and analysis pipelines, plus a user-facing visualization for hospital locations. The work enhances decision support, accelerates onboarding, and lays groundwork for scalable experimentation.
September 2025 monthly summary for d2cml-ai/Data-Science-Python: Delivered foundational data science capabilities enabling end-to-end financial and social data analysis, and established project scaffolding for Practice 1. Implemented data ingestion and analysis pipelines, plus a user-facing visualization for hospital locations. The work enhances decision support, accelerates onboarding, and lays groundwork for scalable experimentation.
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