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This module aims to provide a foundation in the concepts and techniques of data management and statistical analysis. The module will cover a broad range of foundational areas, designed to provide the tools for practical data analysis. It will include a combination of intuitive explanations, mathematical derivations, and hands-on practical experience.

Analytical examples and practical exercises will support the use of R and STATA for data management and analysis (students are not expected to learn all two programming languages, but will be able to use either).

Core topic areas will include:

• Introduction about distributions and models.
• A basic knowledge of concepts and methods used in statistical modelling.
• The principles of data analysis and coding best practices.
• A foundation on which to build detailed knowledge appropriate to particular research interests, the role of statistical methods in epidemiological and public health research.
• Linear regression and Generalised Linear Models.
• Methods of prediction.
• Survival analysis.
• Problems related to the analysis of observational data (missing values, unmeasured and mismeasured confounding). Introduction of methods that can be used to account for possible bias.
• Causal inference – how to deal with time fixed confounders.

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

1. Ask the well-defined questions in order to receive the appropriate information from the data.

2. Perform steps necessary to clean and prepare data for analysis and for writing - and sharing - clean, reproducible code.

3. Apply basic descriptive statistical approaches to understand their data.

4. Apply regression modelling approaches in basic settings.

5. Choose an appropriate statistical approach for a given problem.

The module will be based on a mixture of lectures, practicals, eLearning and educational videos with references to recently published books and peer reviewed papers.

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