Outline: Introduction to Data Science in Business; Data and Data formats; Databases / SQL; Decision trees; Logistic regression; Comparing models; Data products; Nearest neighbours; Clustering; Business strategy.

Assessment Strategy

A report and a computer-based examination.

Excellent: A- to A+ (70%+): Outstanding performance. The relevant information accurately deployed. Excellent grasp of theoretical/conceptual/practice elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Strong evidence of the use of creative and reflective skills.

Good: B- to B+ (60-69%): Very good performance. Most of the relevant information accurately deployed. Good grasp of theoretical/conceptual/practical elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Evidence of the use of creative and reflective skills. of the use of creative and reflective skills.

Satisfactory: C- to C+ (50-59%): Much of the relevant information and skills mostly accurately deployed. Adequate grasp of theoretical/conceptual/practical elements. Fair integration of theory/practice/information in the pursuit of the assessed work's objectives. Some evidence of the use of creative and reflective skills.

Threshold: D- to D+ (40-49%): No major omissions or inaccuracies in the deployment of information/skills. Some grasp of theoretical/conceptual/practical elements. Integration of theory/practice/information present intermittently in pursuit of the assessed work's objectives.

Learning Outcomes

• Apply appropriate statistical learning techniques using the R programming language to solve problems within a business and finance context.

• Apply appropriate tools and techniques to access data from business and financial databases using SQL, APIs and traditional formats.

• Apply data visualisation techniques to communicate solutions to stakeholders in a business and finance context.

• Communicate technical solutions to non-technical stakeholders in a professional, concise manner.

• Create data-driven statistical models to address big data focused business and finance challenges.