Subjects overview
This program can be completed in 4 months (1 semester)
This program can be completed in 4 months (1 semester)
Students must choose thirty credit points (30CP) of subjects from the following electives.
Data analytic skills are core for accounting practitioners in our data-intensive business environment. In this subject, we build on advanced data and analysis concepts to develop an analytical mindset where students learn to frame complex managerial questions, assemble the data, compute relevant metrics and models, identify actionable insights, and design effective and efficient communication of the outcomes. Students explore and apply these skills in a variety of contexts, including management accounting, tax, audit and assurance, and financial statement analytics to develop practical skills in working with multiple analytics tools and develop skills in critically evaluating which tool is best suited for a particular problem or question. Finally, students will explore how to best interpret and communicate the results from data analytic procedures using visualisations.
Read moreThe aim of this subject is to provide a grounding in the principles of modelling as applied to actuarial work – focusing particularly on stochastic asset liability models and the valuation of financial derivatives. These skills are also required to communicate with other financial professionals and to critically evaluate modern financial theories.
Read moreThe focus of this subject is stochastic processes that are typically used to model the dynamic behaviour of random variables indexed by time. The close-of-day exchange rate is an example of a discrete-time stochastic process. There are also continuous-time stochastic processes that involve continuously observing variables, such as the water level within significant rivers. This subject covers discrete Markov chains, continuous-time stochastic processes and some simple time-series models. It also covers applications to insurance, reinsurance and insurance policy excesses, amongst others.
Read moreThe focus of this subject is analysing the time until an event happens, such as the illness or death of a person, or the failure of a business. The issue of censored data is common in such scenarios and how to handle censored data will be discussed throughout this course. The theory, estimation and application of a variety of survival models for censored data are covered, spanning parametric, semi-parametric and non-parametric models. Machine learning methods suitable for censored data are also covered.
Read moreAll organisations today face cyber and fraud threats: small and large businesses, non-profits, health organisations, government and more. Valuable corporate data is highly sought after in the criminal and business communities. Emerging intellectual property and organisational data provides an insight into competitors as well as being valuable commodities to sell on the criminal markets. In this subject, you will be introduced to cybercriminals, learn their motivations and methodologies, and identify potential vulnerabilities and proactive strategies to protect the organisational network, its employees and its data.
Read moreUsing an information systems approach, this subject outlines the design principles and techniques necessary to produce appropriate infrastructure specifications for different data analytic systems. These requirements can be specified in terms of people, procedures, data, software, and hardware. Successful designs will allow systems to automatically extract insights from vast amounts of available data. Topics include, but are not limited to, key modern issues such as job roles in data analytic ecosystems, the operation of organisations, security and data integrity principles, business processes, blockchains, NoSQL databases, cloud solutions, software options and fundamental tenets of computing. The knowledge of these, and understanding how the components interact together, allow students to design efficient systems that are robust to change and conform to best practice.
Read moreUnprecedented volumes of data are being created on an almost daily basis and the amount of data we generate is expected to double every two years. This ‘Big Data’ has the power to change the way we work, live, and think. This subject is designed to provide students with the knowledge and skills to analyse Big Data in a variety of business contexts. Specifically, mathematical and practical applications of Artificial Neural Networks, Support Vector Machines, Natural Language Processing and Ensemble Decision Tree techniques are explored. Valuable skills in the use of these techniques are reinforced with practical application.
Read moreThis subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered. All techniques will be investigated via practical application on real data using the statistical software package R.
Read moreThis subject provides the opportunity to learn the tools and strategies used by investment and hedge fund managers to invest and trade in a number of financial instruments, including equities, futures, FX and ETFs in both low and high-frequency environments. Using financial data drawn from a variety of sources including Bloomberg, you will learn to model and benchmark these strategies using Python. The overall aim of this applied, research-focused subject is to explore quantitative trading strategies used to capitalise on market anomalies.
Read moreThis subject is designed for students who already have a basic understanding of machine learning and want to deepen their knowledge using more advanced techniques. The subject focuses on advanced machine learning methods that are relevant and effective in many real-life and business applications. Students will be provided the necessary tools to wrangle data, implement and train machine learning models, and evaluate the performance and feasibility of these models in the context of the environment where these models are going to be applied. Advanced visualisation tools will be used to create dynamic visual representations of data.
Read moreMany types of economic and financial data naturally occur as a series of data points in temporal order. Stock market indices are a classic example of such time series. Standard statistical methods are not appropriate for such data. This subject provides an introduction to time series econometrics with an emphasis on practical applications to typical economic and financial issues. Emphasis will be placed on determining when it is appropriate to use the various time series econometrics techniques and the use of appropriate software to conduct the analysis.
Read moreMarketing is based on the principle of providing value to customers. To provide value, we need to know what customers need and want; what they know, think and feel about our brand; and how they are likely to behave. Market research refers to the various tools and techniques used in the collection, analysis and interpretation of data to facilitate marketing decision making. This subject will provide you with a theoretical understanding of market research as well as give you practical, hands-on experience collecting, analysing and interpreting data to making more effective decisions.
Read moreStudents must choose ten credit points (10CP) of a postgraduate subject from across the University.
Students may choose from all postgraduate subjects across the University that are available as general electives.
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Take the guess work out of planning your study schedule. Your program's study plan has been carefully curated to provide a clear guide on the sequential subjects to be studied in each semester of your program. Your study plan is designed around connected subject themes to equip you with the fundamental knowledge required as you progress through your course.