MSc Health Data Science

Year of entry: 2022

Coronavirus information for applicants and offer-holders

We understand that prospective students and offer-holders may have concerns about the ongoing coronavirus outbreak. The University is following the advice from Universities UK, Public Health England and the Foreign and Commonwealth Office.

Read our latest coronavirus information

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

Overview

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. 

Aims

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

Return to course details