Overview

This module covers the fundamentals of data analytics, data management, data preprocessing, predictive modelling, unsupervised learning, advanced analytics in the context of supporting business decision making. It develops skills that are in high demand from employers.

Topics may include Data and Data formats, Decision Trees, Logistic Regression, Overfitting and Data Products, Visualisation and Data Products, Similarity and Nearest Neighbours, Clustering and Business Strategy.

Assessment Strategy

Threshold C- to C+ (50-59%): Satisfactory performance. 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. Knowledge of key areas/principles only. Weaknesses in understanding of some areas. Limited evidence of background study. Answer inadequately focused on task and with some irrelevant material and poor structure. Arguments presented but lack coherence. Minor factual/computational errors. Lacking original interpretation.

Good B- to B+ (60-69%): 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. Understands most but not all concepts/issues. Evidence of background study. Focused answer with good structure. Arguments presented coherently. Mostly free of factual errors. Some limited original interpretation. Well known links between topics are described. Problems addressed by existing methods/approaches. Good presentation with accurate communication

Excellent standard: 70+ An outstanding performance, exceptionally able. The relevant information is 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.

Learning Outcomes

• Demonstrate proficiency in the application of statistical learning techniques using the R programming language.

• Execute data visualisation techniques to communicate solutions to management.

• Implement data-driven statistical models to address big data focused business problems.

• Interpret and compare a variety of data analysis techniques, such as data management, data preprocessing, classification and clustering, prediction and forecasting, association rule mining.

• Produce and present technical solutions to non-technical stakeholders in a professional manner using concise written reports.