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This subject equips students with the foundations and tools needed for a career in business analytics. The subject has five distinct components discussed below.
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 Analytics models can be used to predict a performance variable. But many business decisions are not about predicting performance per se. They are about choosing the values of key inputs, such as price or advertising spend, to optimise performance. This requires that the effects of the inputs, as coded by the model, are causal. This typically requires further assumptions about how the data was generated.
The gold standard for establishing causality is a randomised experiment, which is becoming more common in business contacts. The course covers basic principles and practice of experimentation from A-B testing to randomised incomplete block designs. All these methods give rise to estimates of causal effects.
When data is not from an experiment, associations may be entirely spurious. There are several methods that can identify causal effects, such as controlling for known confounders or identifying instrumental variables. When data is collected over time, the so-called error correction model can be used to support the case for causality.
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 Personal Effectiveness 2 and will be partially integrated into the other components of Analytics Applications. 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:
- Communication skills: These skills include effective presentations, verbal communication, written communication, public speaking, and communicating technical material to non-technical audiences.
- Career development skills: These skills include case practice, interview skills including data presentation skills (i.e. data visualisation), CV writing, networking, business etiquette, and data visualisation techniques and systems.
- Team skills: These skills include managing conflict, cultural awareness, giving and receiving feedback, and resilience.
Intended learning outcomes
- Demonstrate how to quantitatively analyse large datasets and convert raw data into relevant information for management decisions, using a wide variety of parametric and non-parametric techniques.
- Understand the difference between supervised and unsupervised statistical learning.
- Determine which techniques to apply to different types of data.
- Understand how to perform model averaging.
- Understand the key reasons that associations in non-experiment data may be spurious and to critique analyses that do not take this into account.
- Be familiar with the key principles of experimental design and how to analyse them.
- Be able to control for confounders and explain why the estimates obtained are more likely to be causal.
- Use the causal estimates to obtain an optimal decision.
- Understand a wide range of models and methodologies relevant to predicting business outcomes.
- Apply appropriate modelling and forecasting techniques to business and economic contexts, and to critique and compare competing methodologies.
- Translate forecasting outputs to information and provide recommendations to address the relevant business problems.
Text and Web Analytics
- Develop and evaluate computational models of language.
- Articulate issues relevant to the efficient implementation of language processing systems and text retrieval systems.
- Apply natural language processing and information retrieval methodologies to textual data.
Personal Effectiveness 2
- Appreciate the importance of communication, career development, and team skills in career success.
- Be able to write a technical report appropriate for a non-technical audience.
- Have improved their communication, career development, and team skills.
- Develop presentation skills to convey technical information to a non-technical audience.
- Be more comfortable giving feedback.
- Understand how to design and evaluate new and innovative data visualisation systems.
- Be familiar with a variety of data visualisation techniques and systems.
Last updated: 29 July 2022