Advanced Business Analytics (BUSA90501)

This subject equips students with the advanced models, methods and tools required for a deep understanding of the latest analytic techniques. The subject has five distinct components discussed below.

Machine Learning

This component builds on the material in Statistical Learning 1 and covers advanced analytic methods. It extends the statistical learning component of Business Analytics Foundations in three ways. First, new techniques such as tree based methods and neural networks are introduced. Second, students will be introduced to unsupervised statistical learning techniques and third, students will learn how to combine models and techniques to produce ensembles with better predictive capabilities.

Data Visualisation

Data visualisation reveals the underlying structure of data sets using representations that utilise the human visual perceptual system. The topics covered include the algorithms and systems for visually exploring, understanding, and analysing large, complex data sets. This includes the visualisation of multivariate, temporal, text-based, geospatial, hierarchical, network and graph-based data.

Predictive Analytics

Predicting key business and economic variables is increasingly important, as it drives both objective decision-making and improved profitability. This component aims to cover the main methods used to predict business and economic variables, based on historical data. These include traditional regression, time series, multivariate and econometric models, as well as emerging methods such as ensemble forecasts. Both point and density prediction will be considered, along with metrics for the quality of both. Throughout, the focus will be on introducing methods in the context of substantive business and economic problems, using a wide range of prediction methods. The importance of benchmarking different methodologies, and the use of prediction in decision-making frameworks, will also be stressed.

Text and Web Analytics

This component helps students develop an understanding of the key algorithms used in natural language processing and text retrieval, for use in a diverse range of applications including search engines, cross-language information retrieval, machine translation, text mining, question answering, summarisation, and grammar correction. Topics to be covered include text normalisation, sentence boundary detection, part-of-speech tagging, n-gram language modelling, sentiment analysis, web mining and analysis, network analysis (including social network analysis), and text classification.

Personal Effectiveness 2

This component builds upon Personal Effectiveness 1 and will be partially integrated into the other components of Advanced Analytics. This component is designed to help students develop the skills and knowledge required to effectively manage the early stages of their career. The “Personal Effectiveness Program” runs across the course and identifies specific needs of each individual student and then provides ongoing support, training, and opportunities to practice and perfect these skills. The program focuses on three core areas:

1. Communication skills: These skills include effective presentations, verbal communication, written communication, public speaking, and communicating technical material to non-technical audiences.
2. Career development skills: These skills include case practice, interview skills, CV writing, networking, and business etiquette.
3. Team skills: These skills include managing conflict, cultural awareness, giving and receiving feedback, and resilience.