- Module code
- Taught during
- Session Two
- Module leader
- Dr. Holger Kunz, Dr. Spiros Denaxasis
- Yes, please refer to module pre-requisites below.
- Assessment method
- Practical Assessments (50%), Final test (50%)
Health Data Science and Data Analytics in Healthcare
Upon successful completion of this module, students will:
- Have a sound understanding of the field of health data science and develop the ability to analyse real-world data using some of its main methods;
- Applying classification to predict class labels for new feature vectors;
- Become comfortable applying regression models for continuous and limited outcome variables;
- Explore more complex models such as ensembles;
- Develop familiarity with descriptive and predictive analytics, and their application to big health data problems;
- Explore methods of unsupervised learning and clustering;
- Have received a solid foundation for more advanced or more specialised study and research.
This is a level two module (equivalent to second year undergraduate). In addition to the standard UCL Summer School entry criteria, applicants will be expected to have successfully completed at least one undergraduate level module either in biomedicine or in a quantitative subject.
Classes (usually three or four hours per day) take place on the Bloomsbury campus from Monday to Friday any time between 9am and 6pm.
- Practical assessments (50%)
- Final test (50%)
Holger Kunz is a computer scientist and Teaching Fellow at the Institute of Health Informatics, UCL. He has conducted research in applied machine learning for medical imaging and the treatment of eye tumours. He has also conducted data science research for clinical indicator systems and quality management/dashboards in a hospital setting and in the field of eHealth for health and wellbeing and disease prevention. He has presented his research at international conferences in Vancouver, Sydney, Portland, Lyon and Glasgow. He is passionate about health informatics and to improve the health of patients with data-driven methods.
Spiros Denaxasis an Associate Professor in Biomedical Informatics based in the Institute of Health Informatics, UCL. His background is in computer science, information systems engineering and bioinformatics. His research lab (http://denaxaslab.org) operates at the intersection between health research and computer science and focuses on creating and evaluating data-driven methods for transforming electronic health records into research-ready datasets and answering clinically meaningful questions.