This module provides students with a formal understanding of research methods, and develops their ability to critically reflect on research approaches and practices in the field. On completion of this module students will be able: to understand what the data science role entails, and how that individual performs their job within an organisation on a day-to-day basis; to understand how research is performed in terms of formulating a hypothesis and the implications of research findings, and be aware of different research strategies and when these should be applied; to gain an understanding of data processing, preparation and integration, and how this enables research to be performed; to critique research proposals in terms of their ethical implications; to learn how data science problems are tackled in an industrial settings, and how such findings are communicated to people within the organisation.

This is an introductory module on epidemiology with a strong focus on applied and quantitative topics. The module introduces key epidemiological concepts, including types of health outcomes, definitions of exposure and risk, and metrics used to quantify disease in a population. The students will be given an overview of the most commonly used epidemiological study designs: case-control studies, randomized control trials and cohort studies. Limitations and issues arising from recruitment and sampling biases will be discussed for each design. An important topic of the module will be how to draw inferences from epidemiological studies, defining association, causality and how these differ. In the lab sessions of the module, the students will analyse epidemiological data using R statistical programming language.

The module will teach the fundamentals of computing: historical development of computational machines, data storage, computational processing, and the development of computer science. This will be done using the R language for programming with focus on: simple calculations; structured programming with loops and conditions; data structures; writing functions. The students will be taught how to handle the R language for data file input/output; selection and filtering; exploratory graphics. Throughout the module, the students will also learn how to develop good computational research practice: project management for data and code; dynamic documents for reproducible research; source code management. In the second part of the module the students will learn the Python language basics: fundamentals for calculation and programming; writing functions and modules for code re-use; creating new classes and programming with objects; Python including command line and notebooks. The module will conclude with an introduction to numerical Python: using NumPy and Pandas for data analysis; plotting basic graphics.

This module introduces basic methods and models for statistical analysis with a strong focus on epidemiological applications. The module starts with the use of graphical tools for exploratory analysis: scatter plots; box plots; transformation of the response and outcome variables. The linear regression modelling framework is introduced with a focus on: critical evaluation of assumptions; link between regression analysis and ANOVA; interpretation of regression coefficients; multicollinearity and dealing with confounding factors; analysis of residuals. Building on this, the module covers Binomial and Poisson regression, and students will learn how to carry out hypothesis-testing through the analysis of deviance and the use of regression diagnostics. In the final part, the module gives an introduction to linear mixed models (random intercept and slope) and how to deal with overdispersion of count data.

The module will begin by outlining the reasons for conducting economic evaluations with reference to economic concepts such as opportunity costs and marginality. Students will then how to conduct economic evaluations. The focus will be on how to analyse data from a trial-based economic evaluation, on how to conduct a model-based economic evaluation using, for example, decision trees or Markov models, and on how to characterise uncertainty using, for example, probabilistic sensitivity analysis.

The module will give students an understanding of the nature of routinely collected NHS data including ways of coding it (such as the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10)). Furthermore there will be exploration of other routinely collected data sets such as those held by the Office of National Statistics. Alongside this the module will cover relevant structures within the NHS with particular reference to data collection, coding and analysis as well as information governance and ethics. Students will also explore stakeholder engagement in the NHS including business intelligence and clinical staff. The module will cover approaches to identifying research priorities in the NHS. Methodologically, students will learn about methods such as systematic review and meta-analysis and standard methods for data cleaning and preparation (such as methods for identifying missing data and data engineering).

Almost every set of data, whether it consists of field observations, data from laboratory experiments, clinical trial outcomes, or information from population surveys or longitudinal studies, has an element of missing data. For example, participants in a survey or clinical trial may drop-out of the study, measurement instruments may fail, or human error invalidate instrumental readings. Missingness may or may not be related to the information being collected; for instance, drop out may occur because a patient dislikes the side-effects of an experimental treatment or because they move out of the area or because they find that they no longer have the time to attend follow up appointments. In this module you will learn about the different ways in which missing data can arise, and how these can be handled to mitigate the impact of the missingness on the data analysis. Topics covered include single imputation methods, Bayesian imputation, multiple imputation (Rubin's rules, chained equations and multivariate methods, as well as suitable diagnostics) and modelling dropout in longitudinal modelling.

Using motivating examples from environmental and health sciences, this module first introduces key data concepts of geostatistical analysis. The students will then learn how to perform the different stages of a geostatistical analysis, including: spatial exploratory analysis, model formulation, parameter estimation and spatial prediction. Building on the students' knowledge of standard linear regression, they will learn how to formulate and apply linear geostatistical models using maximum likelihood and Bayesian methods of estimation. The module also introduces generalized geostatistical models, with a special focus on Binomial logistic and Poisson log-linear models and their applications to disease prevalence mapping and health surveillance.

Hierarchical data arise in a multitude of settings, specifically whenever a sample is grouped (or clustered) according to one or more factors with each factor having many levels. For instance, school pupils may be grouped by teacher, school and local education authority. There is a hierarchical structure to this grouping since schools are grouped within local education authority and teachers are grouped within schools. If multiple measurements of a response variable, say test score, are made for each pupil across multiple measurement times, the data are also longitudinal. This module motivates the need for statistical methodology to account for these kinds of hierarchical structure. The differences between marginal and conditional models, and the advantages and disadvantages of each, will be discussed. Linear mixed effects models (LMMs) for general multi-level data will be introduced as an extension to the linear regression model. Longitudinal data will be introduced as a special case of hierarchical data motivating the need for temporal dependence structures to be incorporated within LMMs. Finally, the drawbacks of LMMs will be used to motivate generalised linear mixed effects models (GLMMs), with the former a special case of the latter. GLMMs broaden the scope of data sets which can be analysed using mixed-effects models to incorporate all common types of response variable. All modelling will be carried out using the statistical software package R.

This module will start by introducing the mathematical aspects of deterministic and stochastic infectious disease models. The theoretical lectures will be complemented with lab sessions for the implementation and simulations of mathematical models in R. The module will then focus on fitting of infectious disease models to observed epidemiological data in R.

Management of infectious disease datasets in R, display and presentation of complex model-based outputs is a key component of the module.

Building upon your existing knowledge, this module starts by illustrating the limitations of standard linear regression to analyse time series data and how to question the assumption of independence through the analysis of residuals. In this context, you will learn how to use graphical tools for exploring time series analysis, such as lag-plots and autocorrelation plots. The modelling of seasonal effects, inherent to many environmentally driven diseases, will be taught based on harmonic regression methods. Following this, the module will give an introduction to the theory of discrete time series models and will focus on simple autoregressive processes. Continuous time series models based on Gaussian processes will be introduced to analyse time series for counts data. Finally, the module demonstrates how the methods learned can be used for forecasting problems, with applications to the detection of disease outbreaks.