Business Intelligence, including framework design and architecture and the mastering of data management.

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Database fundamentals and warehouses.

Dimensional modelling (building blocks of data models).

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Relational databases.

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SAS macros; SAS SQL; SAS OLAP cubes.

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Pivot tables in MS Excel.

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SAS Enterprise Guide.

Calculate marketing metrics, such as traditional customer metrics, customer acquisition metrics, customer activity metrics, value metrics, etc.

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Conduct recency, frequency, monetary-value (RFM) segmentation.

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Understand customer lifetime value (CLV), such as past customer value, formulate CLV, understand and apply retention and migration CLV models, etc.

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Analytical customer relationship modelling (CRM) techniques to manage customers, such as customer acquisition and costs, customer retention – cross selling and up-selling, balancing acquisition and retention, customer churn prediction and reduction to churn.

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Extensions of the RFM model.

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Estimate revenue streams.

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Data ethics.

Critical understanding and development of predictive models (i.e. scorecards) in the field of retail credit risk.

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Employ supervised and unsupervised learning methods.

Specialised knowledge with regards to the use of logistic regression as a supervised method in the field of retail credit risk.

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Credit risk management.

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Design and develop scorecards to solve problems in the field of retail credit risk.

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Unsupervised methods such as clustering used for scorecard development.

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Conduct research according to standard protocol and employ appropriate protocols, conventions, processes, procedures and techniques to solve problems in the field of credit risk.

The ability to identify, select, apply, interpret, and critically judge the appropriateness of a range of mathematical programming formulations in solving complex optimisation problems relevant in finance.

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Use the designated software package to capture the mathematical models associated with a specific problem, apply suitable optimisation algorithms to find solutions, and select the most effective course of action based on a critical assessment of the results.

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Study the correct use of terminology appropriate to the field of multicriteria decision making.

Practical project management, such as formulation, planning, scheduling, costing, scoping, execution and monitoring, documenting and presenting results.

Critical path analysis.

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Identify, formulate and solve business problems using quantitative and qualitative tools.

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Demonstrate creative insight, rigorous interpretation of solutions with the development of technical writing skills.

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Manage project from conception to execution.

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Meeting management and etiquette (agendas, minutes, meeting documentation packs, group work, and professionalism).

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Write proposals and effectively document project results.

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Effective listening skills development.

Data preparation, such as transformations and the incorporation of non-numeric data.

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Data mining principals and models.

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Conceptually design and develop data mining models.

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Variable selection, categorical input consolidation.

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Analysis of decision trees, regression analyses and neural networks.

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Model assessment and implementation.

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Pattern discovery and cluster analysis.

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Ensemble and surrogate modelling.

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The use of high performance distributed computing.

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Apply random forests, bagging, boosting.

Integrated knowledge and understanding of practical project management, such as formulation, planning, scheduling and costing of the project, the determination of a base line, the execution and monitoring of the project, documentation and the presentation of the results, etc.

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Identify, formulate and solve business problems using appropriate qualitative and quantitative tools.

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Effectively present and communicate, orally and in writing, relevant academic and professional information – including creative insight, rigorous interpretations, and solutions to problems – to a range of audiences with the use of appropriate technology.

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Operate independently and take full responsibility for his or her own work. Individually manage a project from conception to execution.

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Effectively manage meetings through tools such as meeting agendas, minutes and meeting document packs. Demonstrate high levels of autonomy and initiative in research and professional conduct.