Data Warehousing and Predictive Analytics
| Course Code | COMP-309 |
|---|---|
| Lecture hours per week | 2 |
| Lab hours per week | 2 |
| Course Availability | Open |
| Description | This course builds on students’ knowledge of databases and programming to develop modern data warehousing, cloud-based data engineering, business intelligence, and predictive analytics solutions. Students learn how to collect, integrate, transform, store, analyze, and visualize data from multiple sources using contemporary data engineering practices, cloud-based analytical platforms, and enterprise business intelligence tools. Topics include data warehouse design, data integration and transformation, data lakes, predictive modelling, machine learning, model evaluation, data visualization, and the deployment of machine learning services for intelligent decision support. The curriculum also introduces modern AI-enabled analytics workflows and demonstrates how predictive models can be integrated into software applications and data-driven services. Students apply responsible AI-assisted development practices to support data preparation, analysis, model development, testing, visualization, documentation, and validation while applying critical evaluation, human oversight, security, privacy, ethics, and professional judgment. Through hands-on laboratories and analytical projects using real-world datasets, students develop practical skills for designing, implementing, and deploying scalable analytical solutions that transform data into actionable business insights. Prerequisites
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