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In this module you will be introduced to the core themes of public health and how data science can be used to promote, protect health and well-being, prevent ill-health and prolong life through the organised efforts of society. You will study how public health data science can be applied to improve the health of populations and health services. You will also learn how it can be used to protect the population from health harms, such as outbreaks of disease and pollution. We will cover how epidemiological methods and biostatistics can be applied to public health data science, and look at tools for public health data science. The module will also demonstrate how health data can be visualised using mapping techniques.

You are required to have prior knowledge of R. Materials throughout the module and the analysis required for the assessment will be in R. The assessment submission will be in the form of a word document and R script.

At the end of this module you will be able to:

1. Define what is meant by ‘Public Health Data Science’.
2. Describe the basic study designs used in public health.
3. Articulate a basic understanding of epidemiology and biostatistics methods that are needed in Public Health Data Science.
4. Access and appraise a range of data sources commonly used to describe human populations and their health.
5. Describe the analytical tools used in Public Health Data Science and use one or more of the tools.
6. Design a small study including analysis of individual-level data to answer a public health question.
7. Critically evaluate the use of data visualisation and mapping methods to a public health task.
8. Discuss translation of public health data science into policy and the interface between data scientists and policy organisations.

The module is a blended learning module delivered through web-based distance learning in the UCL Virtual Learning Environment plus a 3-day face-to-face teaching session.

Grolemund G. (2017) R for Data Science. O’Reilly.

Donaldsons L. (2017) Donaldsons' Essential Public Health, CRC Press.

Altman D. (1990) Practical Statistics for Medical Research. Chapman & Hall.