MSc Health Data Science / Course details

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

• 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

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.

• 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

• 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

• 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

• 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

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)

• 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)