nikbearbrown/INFO_7390_Art_and_Science_of_Data
Apr 2025 – Apr 2025
1 Month active
Languages Used
DockerfileJupyter NotebookMarkdownPython
Technical Skills
Anthropic Claude APIBackend DevelopmentData AnalysisData ScienceDeep LearningFile Upload
Jimmy Yifan-Yuan
PROFILE
Jimmy Yifan-yuan
Yuan Yifa contributed to the nikbearbrown/INFO_7390_Art_and_Science_of_Data repository by developing features that enhance data analysis and model fine-tuning workflows. Yuan built a Streamlit-based data exploration tool integrating the Anthropic Claude API and Model Control Protocol, enabling users to analyze local CSV files through natural language prompts. They also authored parameter estimation documentation with visualizations, clarifying statistical concepts for analysts. Additionally, Yuan fine-tuned a language model on the Alpaca dataset using Python and the TRL SFTTrainer, improving task performance. Their work demonstrated depth in backend development, data science, and natural language processing, while maintaining clear, user-focused documentation.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
15,860
Activity Months1
Your Network
133 people
Work History
April 2025
5 Commits • 3 Features
Apr 1, 2025April 2025 Monthly Summary for nikbearbrown/INFO_7390_Art_and_Science_of_Data: Delivered key features and fixes across documentation, data exploration, and model fine-tuning. Highlights include Parameter Estimation Documentation with visualizations; Data Exploration Tool leveraging MCP and Claude via a Streamlit client-server setup; Fine-Tuning of a language model on the Alpaca dataset using TRL SFTTrainer; and documentation cleanup removing an outdated Parameter Estimation guide for Causal Inference. These efforts drive faster decision-support, self-serve analytics, improved model task performance, and reduced user-facing documentation noise.
5 Commits • 3 Features
Apr 1, 2025April 2025 Monthly Summary for nikbearbrown/INFO_7390_Art_and_Science_of_Data: Delivered key features and fixes across documentation, data exploration, and model fine-tuning. Highlights include Parameter Estimation Documentation with visualizations; Data Exploration Tool leveraging MCP and Claude via a Streamlit client-server setup; Fine-Tuning of a language model on the Alpaca dataset using TRL SFTTrainer; and documentation cleanup removing an outdated Parameter Estimation guide for Causal Inference. These efforts drive faster decision-support, self-serve analytics, improved model task performance, and reduced user-facing documentation noise.
April 2025
Activity
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Quality Metrics
Correctness76.0%
Maintainability76.0%
Architecture76.0%
Performance72.0%
AI Usage48.0%
Skills & Technologies
Programming Languages
DockerfileJupyter NotebookMarkdownPython
Technical Skills
Anthropic Claude APIBackend DevelopmentData AnalysisData ScienceDeep LearningFile UploadFine-tuningFull Stack DevelopmentMCPMachine LearningNatural Language ProcessingPythonStatisticsStreamlitTransformers
Repositories Contributed To
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