Synthetic data project framework

Our company is interested in creating frameworks / templates for our pilot projects with social impact clients. Impact of this work is to maintain efficient operational execution in how we structure our client work. We would like to collaborate with students to provide cohesive, appropriate details relative to the pilot project scaffolding from our Data Scientists. Students will write technical statistical documentation on methodologies and write code (likely in Python, using Jupyter and/or Marimo notebook) so we can achieve a fulsome pre-pilot understanding of the workflow involved. This will involve several different steps for the students, including: Working with Data Scientist guidance to write code that assesses initial data type, structure, etc, especially on determining appropriate analytical tooling for client data use case categories Expanding on baseline Data Scientist framework document for synthetic data 'utility' metrics, i.e. how useful to a given statistical model the synthetic data is Completing all above in analytical objectives including but not limited to dimension reduction, relationship analysis, and clustering

AI-Powered Financial Analysis System

The project aims to develop an AI-powered financial analysis and trading assistant designed to evaluate market momentum and provide short- to mid-term forecasts and strategies. The assistant will analyze real-time stock market data, financial news, and economic indicators to identify trends, investment opportunities, and potential risks. By leveraging AI-driven scoring and sentiment analysis, the system will offer buy/sell signals, market sentiment insights, and strategic recommendations to guide decision-making. The solution will be hosted on a secure cloud platform to ensure accessibility and scalability. This project provides learners with an opportunity to apply their knowledge of AI, data analysis, and financial markets to create a tool that simplifies market analysis and enhances investment strategies.