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Medical statistics plays an essential role in all stages of a quantitative health care research project from design through to analysis and interpretation. This intensive course covers the essential principles and methods required. Emphasis is on study design, appropriate analysis, and interpretation of results. The underlying concepts of statistical analysis as well as basic and some more advanced analysis techniques are covered. Sessions include lectures and practical work, both computer based (using Stata) and using small workshops for discussion. The course has been running for more than 20 years and has earned an international reputation. Lectures are taught by experienced medical statisticians from renowned academic institutions.
The rapid growth of the Personal Genetic Testing (PGT) market raises a number of important scientific, ethical, legal and social concerns, including data security, privacy, and identity, as well as issues around the accuracy, utility, and communication of inferences regarding ancestry, biological predispositions, disease vulnerability, and the sharing of personal data with third parties.
Database systems are increasingly being used for working
with medical data and enable the rapid querying of complex data in health and
social care. This short course will introduce the theory behind the relational
data model and enable participants to gain an understanding on how data can be
modelled and stored in a relational database system and what different data
types are used. Through a series of practical-driven sessions using real-life
data, students will learn how to load existing data in a contemproary
relational database management system and how to craft simple and complex
queries for analysing the data. By the end of the course, students will be able
to load, format and export data in a format suitable for analysis by common
Everything is affected by the digital revolution. The opportunities
for interdisciplinary digital health research bringing together computer
science to dramatically improve public health, global health and
wellbeing of individuals and populations globally are extraordinary.
the last 5 years, I have been working to build and deploy context-aware mobile
applications, based on sensor data from personal mobile & wearable devices.
In this talk, I will detail two key themes of my research. First, I will
describe work on wearable analytics that uses sensor data from personal wearable
devices (e.g., smartwatches) to capture fine-grained insights on daily
lifestyle activities, such as eating.