MSc Health Data Science

Year of entry: 2022

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Course unit details:
Machine Learning and Advanced Data Methods

Unit code IIDS67682
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Division of Informatics, Imaging and Data Sciences
Available as a free choice unit? No


Biomedical and health informatics is founded on the usage and application of computational algorithms to high-throughput biological data and patient-level information. With the growing availability of big data in the biomedical domain, machine learning and other advanced approaches are becoming essential. Informaticians need to have a good understanding of these different algorithms, methodologies and analysis pipelines, and their applicability to different data types and research questions. 


This unit aims to:

  • Introduce different types of biomedical and health data, including from high-throughput biological experiments and patient-level information resources
  • Examine different approaches and pipelines for data analysis
  • Learn the principles underlying popular machine learning algorithms
  • Gain experience in applying machine learning to real-world datasets
  • Critically appraise analysis pipelines and data usage and be able to make suggestions on improvements

Teaching and learning methods

This unit will be delivered in a face-to-face format over six days: lectures and open discussions will provide basic and core knowledge and introduce concrete examples and encourage attendees to draw upon their own reading and experience. Practical sessions will provide hands-on experience, with real-world application of the theoretical material. Coding will be carried out using python via web-based jupyter notebooks that will be available to work on throughout the course. All materials will be introduced on Blackboard.

Knowledge and understanding

  • Demonstrate a critical understanding of advanced machine learning methods and techniques
  • Apply these methods to a range of problems
  • Evaluate different scenarios in which such methods would be applicable
  • Assess the possible ethical implications of Machine Learning methods when applied to biomedical data sets

Intellectual skills

  • Critically evaluate the strengths and limitations of different data analysis methods and approaches
  • Apply analytical methods to a range of datasets and research questions
  • Demonstrate an understanding and draw conclusions from the results of analytical methods

Practical skills

  • Produce and execute a data analysis pipeline using python
  • Carry out exploratory data analysis using visualisation and clustering methods
  • Apply and evaluate the performance of different machine learning algorithms

Transferable skills and personal qualities

  • Improved coding skills and experience with Jupyter notebooks
  • Be able to apply data analysis skills to a broad range of problems
  • Use appropriate software and tools for data analysis in biomedical research

Assessment methods

3 x interactive online assessments

Three online assessments carried out through interactive notebooks

The three assessments will cover:

1. Exploratory data analysis

2. Supervised machine learning

3. Advanced topics (neural networks and ethics)

Recommended reading

  • Elements of Statistical Learning (Hastie, Tibshirani & Friedman, Springer, 2009, second edition)
  • Machine Learning in Healthcare Informatics (Dua et al., Springer, 2014)
  • Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Panesar, Apress, 2019)

Study hours

Scheduled activity hours
Practical classes & workshops 25
Tutorials 25
Independent study hours
Independent study 100

Teaching staff

Staff member Role
David Wong Unit coordinator
Magnus Rattray Unit coordinator

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