Data analysis is now a crucial part of the modern economy, influencing decision-making in both industry and government. This module will equip students to analyze data descriptively and apply inferential statistical techniques to data, as well as understanding and critiquing the analyses of others.

The syllabus might vary somewhat from year to year, but typically the topics covered will include the following: Data Sources and Structures Managing data - Accessing online databases - Handling different data formats - Re-structuring data Advanced Excel Methods Descriptive Statistics: - Means, medians and modes. Measures of dispersion - Cross tabulations and correlations Inferential Statistics - Confidence intervals; hypothesis testing - Testing for differences between groups Data Visualisation - Univariate Graphs: Histograms; Boxplots; Density functions - Bivariate graphs Linear regression - Ordinary Least Squares - Interpretation of results - Model diagnostics

On successful completion of this module, students will be able to 1. find sources of economic data 2. explain the principal methods of analysing data (e.g. the intuition underlying hypothesis testing and OLS estimation) 3. present economic relationships and estimates in words, graphically, and through equations. 4. analyse economic data using appropriate models and methods (e.g. linear regression) using statistical software. 5. interpret the results of statistical analysis and estimated models (e.g. hypothesis testing and OLS estimates).

On successful completion of this module, students will 1. demonstrate an appreciation of the limits of statistical analysis, especially inferring causality in a statistical association 2. display ethical standards in the representation of statistical outcomes 3. judge the quality of sources of economic data

On successful completion of this module, students will be able to: 1. prepare and analyse data using software

This module will be delivered online through a blended learning approach of synchronous and asynchronous learning events, including pre-recorded lectures, live online computer tutorials and live online trouble shooting webinars designed to maximize student participation and contribution. The module consists of lectures discussing conceptual and practical aspects of empirical research. The tutorials will further demonstrate the application of empirical techniques and the students will apply these techniques to specific datasets. Online statistical software courses, to be taken during the students' private study time, will further develop their statistical software skills. Students have the option of continuing these online statistical software courses beyond the assigned set of courses. Thus, students take a pro-active approach to building their portfolio of software experience.