“Generating new and actionable business insights and supporting critical business decisions through real-world data science projects.”
The Data Science Lab at CGU is a research and innovation hub innovation where students and faculty engage in cutting-edge research at the intersection of data science, AI, and analytics. Through interdisciplinary projects, industry partnerships, and applied methodologies, the lab provides hands-on opportunities for students to bridge the gap between academic theory and real-world impact.
Goal
Foster a team-based learning environment where students apply advanced data science, AI, and machine learning techniques to tackle real-world challenges, drive business insights, and build impactful data-driven solutions.
Approaches
- Apply key data science and AI methodologies, including predictive analytics, machine learning, and statistical modeling, to real-world research and projects.
- Leverage strong statistical and machine learning skills alongside software engineering and data management to build, evaluate, and deploy scalable analytical solutions.
- Develop AI-powered models and decision-support systems that drive innovation, optimize decision-making, and create measurable impact in real-world applications.
Distinguished Features
- Led by expert faculty with deep expertise in machine learning, AI, and data science techniques, guiding students in transformative research and real-world applications.
- Focuses on process-driven analytics, integrating best practices in data management, machine learning algorithms, and AI-driven business intelligence.
- Engages in industry collaboration and applied research, allowing students to work on impactful projects across business, healthcare, and technology sectors.
Current Project Samples
- Drucker’s Voice – A generative AI tool built on an Agentic RAG architecture to enhance strategic decision-making and leadership insights.
- CC-DIY – A toolkit for processing and indexing large-scale web data to support domain-specific small language model training.
- DACMM: Data Analytics Capability Maturity Model – A structured framework for assessing and improving data analytics maturity, developed and tested with real-world organizations.
- Digitalization of Health Services in Low-Resource Settings – AI-assisted digital health record solutions for managing patient care in environments with limited internet access.
- Intelligent Systems for Healthcare Accessibility – Machine learning and AI-driven analytics applied to improving healthcare services and decision support for vulnerable populations.