PROGRAMME / 01
Bachelor · Artificial Intelligence and Data Science
Combine statistics, machine learning and data engineering to develop predictive models and deploy intelligent applications.
Progress from foundations to professional practice. Planned workload: 180 credits.
YEAR 01Data and Computing Foundations
- Python Programming
- Linear Algebra, Probability and Statistics
- Data Structures and Algorithms
- Databases and SQL
- Data Cleaning and Visualisation
- Technical English and Academic Communication
YEAR 02Machine Learning and Data Engineering
- Machine Learning
- Statistical Inference
- Foundations of Deep Learning
- Data Engineering and Pipelines
- Software Engineering and Git
- Feature Engineering
YEAR 03Intelligent Analytics and Applications
- Time Series and Forecasting
- NLP and Generative AI
- Introduction to Causal Analysis
- Model Deployment and Interpretability
- Scientific Data Analysis
- Intelligent Application Capstone
SHARED LEARNING
Shared foundations. Specialist depth.
Selected AI, data processing, data engineering and deployment modules may be shared with related PTI programmes.
Specialist focus: Statistical inference, scientific data analysis and reliable prediction.
Assessment and outcomes
Assessment covers data quality, experimental design, generalisation and interpretation. Predictive projects must document data splits, prevent leakage and compare results with appropriate baselines.
Build a reproducible predictive system using public or authorised data. Submit code, data documentation, model evaluation and a technical report.