UUY-DTS 4102 units400 LevelFirst Semester

Special Topics in Data Science

B.Sc. Data Science, University of Uyo

A rotating course on current developments in the field — the content varies year to year with staff expertise and what is moving in the discipline. Recent themes include deep learning, large language models, MLOps and causal inference.

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  • 2 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01Current research directions in data science and machine learning
  2. 02Deep learning architectures: CNNs for images, RNNs/Transformers for sequences
  3. 03Large language models: pretraining, fine-tuning, prompting and retrieval-augmented generation
  4. 04Natural language processing pipelines and embeddings
  5. 05MLOps: experiment tracking, model registries, monitoring and drift detection
  6. 06Causal inference: correlation vs causation, randomised experiments, difference-in-differences
  7. 07Responsible and explainable AI: SHAP, LIME and model interpretability
  8. 08Edge cases and emerging areas: federated learning, synthetic data, AutoML
  9. 09Reading and critiquing a current research paper

Recommended textbooks

  • Deep Learning — Goodfellow, Bengio & Courville

    Free at deeplearningbook.org; the standard reference

  • Designing Machine Learning Systems — Chip Huyen

    The best book on ML in production — the MLOps half of this course

How to pass UUY-DTS 410

  • Confirm this year's actual syllabus with the lecturer in week one — the content genuinely rotates, so last year's notes may not apply
  • Whatever the theme, the assessment usually involves reading current papers; practise the three-pass method from UUY-DTS 323
  • This is the course closest to what employers are hiring for right now — treat it as career investment, not a filler 2 units
  • Reproduce one result from a paper on a small dataset; "I reimplemented it" is a strong thing to say in an interview

Other 400 Level courses

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