Topics covered include:

• The role of the data scientist
• Data strategy and Key Performance Indicators (KPIs)
• Deployment and implementation
• Governance, ethical and cultural implications
• Exploring and describing data
• Statistical inference – parametric methods t – tests and Analysis of Variance Statistical presentation of data
• Multivariate methods – principal component analysis, exploratory factor analysis and segmentation methods (Hierarchical clustering, K means and K modes)
• Statistical modelling – OLS regression, general linear models exemplified by Binary Logistic models
• Diagnosing model fits The R package for statistics will be used in this module

Upon completion of this module you will be able to:

• Critically evaluate the drivers and strategies for advanced analytics and its impact on organisational decision-making
• Critically assess the roles and impact of ethics, governance and professionals in data analysis
• Apply methods of data reduction and of classification to data to identify sub-groups
• Construct and diagnose statistical models to allow prediction of effects and input into strategy development.

How you’ll be taught 

You will be supported by the Global Online team who will provide general overall support, and by the module teams who will provide module-specific online material and discussion forums, using a variety of communication technologies such as Moodle and Skype. You will be encouraged to develop your learning through peer and tutor interaction via electronic communication. Self-study readings, supported by online discussions forum hosted through the VLE, will develop independent learning skills. Formative feedback will be provided via online quizzes. The lecture programme will be enhanced by material from guest speakers and will be made available online. The material for the lab-based practical sessions will be made available online with a support forum.