An interdisciplinary four-year program launched in 2024 that integrates statistics, computer science, and domain-specific applications of artificial intelligence. Students gain hands-on experience with large-scale datasets, machine learning pipelines, and the ethical dimensions of algorithmic decision-making. The program is offered jointly by the Faculty of Science and draws on expertise from Engineering, Medicine, and Business.
| Code | Course Name | Cr. | Year | Prerequisites | Description |
|---|---|---|---|---|---|
| DS 101 | Foundations of Data Science | 3 | Y1 | None | Data types, collection, cleaning, exploratory analysis, and basic visualization with Python and pandas. |
| DS 201 | Statistical Learning | 3 | Y2 | STAT 101, CS 101 | Regression, classification, resampling, regularization, tree-based methods, SVM, and unsupervised learning. |
| DS 210 | Probability & Statistics for Data Science | 3 | Y2 | MATH 101 | Probability theory, distributions, hypothesis testing, Bayesian inference, and simulation. |
| DS 301 | Machine Learning | 3 | Y3 | DS 201, MATH 202 | Supervised and unsupervised learning, neural networks, evaluation metrics, and fairness. |
| DS 310 | Big Data Systems | 3 | Y3 | CS 201 | Distributed data processing, MapReduce, Spark, streaming architectures, and cloud data pipelines. |
| DS 320 | Natural Language Processing | 3 | Y3 | DS 301 | Text preprocessing, embeddings, transformers, language model fine-tuning, and NLP applications. |
| DS 330 | Industry Practicum | 6 | Y3 | DS 201 | Supervised 4-month placement with an industry or research partner. Report and presentation required. |
| DS 401 | Deep Learning | 3 | Y4 | DS 301 | CNNs, RNNs, attention, transformers, generative models (VAE, GAN, diffusion), and scaling laws. |
| DS 490 | Capstone Project | 6 | Y4 | DS 330 | Year-long research or applied project with an industry or academic supervisor. Public poster session. |