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
Contributed to the INFO_7390_Art_and_Science_of_Data repository by delivering three core features and a documentation fix within one month. Developed a data exploration tool using Streamlit and the Anthropic Claude API, enabling natural language analysis of local CSV files through a client-server architecture. Authored comprehensive parameter estimation documentation with visualizations to support decision-making and statistical understanding. Fine-tuned a language model on the Alpaca dataset using Python and TRL’s SFTTrainer, enhancing model task performance. Additionally, improved user experience by removing outdated causal inference documentation, ensuring clarity. Work demonstrated depth in backend development, data science, and natural language processing using Python.
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
159 peopleShared Repositories
18
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Fine-Tuning of a language model on the Alpaca dataset using TRL SFTTrainer; and documentation cleanup removing an outdated Parameter Estimation guide for Causal Inference.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Fine-Tuning of a language model on the Alpaca dataset using TRL SFTTrainer; and documentation cleanup removing an outdated Parameter Estimation guide for Causal Inference.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
Loading activity data...
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
Overview of all repositories you've contributed to across your timeline