Data Science and Deep Learning Across the Alliance: A Personalized Project Activated Modality REU

Research Experiences for Undergraduates (REU) bring challenges while lifting expectations in participants. Our learning model engages hands-on applications on algorithms, datasets, and computer power under the auspices of NSF Florida Georgia Louis B. Stokes Alliance for Minority Participation [FGLSAMP] grant: Developing Researchers through Innovative Virtual Experiences [DRIVE]. The alliance represents institutions at various educational levels and ethnicities (two-year and four-year educational institutions of Hispanic or historically black, or predominantly white). We introduced Data Science and Artificial Intelligence (AI) to advocate for students and mentor minority populations in the era of Machine Learning (ML) and Big Data (BD). We involved an interdisciplinary group of undergraduates [such as Computer Science and Engineering, Information Tech-nology, Biology Pre-Medicine, Software Engineering, Pharmacy, Biology Pre-Dental, and Mathematical Sciences]. By the end of the program, participants integrated AI, ML, and Deep Learning techniques to provide solutions to their projects-self-chosen at the beginning of the REU. This project activated modality began with such topics as Time Series Analysis of Blockchain-Based Cryptocurrency; Analyzing Advantages and Disadvantages of AI for Breast Cancer Detection in Women; AI Analysis of Autism in three different cities, and others. Participants' survey indicated that this was very important and useful for their careers. They reported significant enhancement of essential cognitive and professional skills and enumerable learned skills such as Python, Skit-learn packages to build ML models and Git Github as version control collaborating systems.