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A primary use of data by contemporary organisations is to analyse and explore opportunities for growth or change, either directly or indirectly. The demand for business data, whether operational management, data analytics or data science (such as “big data”, machine learning & predictive analytics) has increased substantially. This has resulted from an organisational need for a more sophisticated approach to analytics and data from both a business and statistical understanding of data and its impacts on the organisation. This raises complex and multifaceted issues.

The aim of the module is to enable you develop a deep understanding of the business context and impact of data, the meaning of the data (including in terms of statistics), and to give you an opportunity to express this in the form of professional written reports. 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.

The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in computational thinking and its relevance to everyday life, critical evaluation and professional considerations and practical skills in the deployment and use of tools and critical evaluation of complex problems in addition to providing useful generic skills for employment.

Upon completion of this module you will be able to:

LO1: Critically evaluate the drivers and strategies for advanced analytics and its impact on organisational decision-making
LO2: Critically assess the roles and impact of ethics, governance and professionals in data analysis
LO3: Apply methods of data reduction and of classification to data to identify sub-groups
LO4: Construct and diagnose statistical models to allow prediction of effects and input into strategy development.