Subjects overview
This program can be completed in 4 months (1 semester)
Data Analytics Options
Students must choose thirty credit points (30CP) of subjects from the following electives.
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ACSC71-200: Mathematical Statistics (10CP)
This is an intermediate level subject in the theory and practice of statistical inference. It extends STAT11-112 in the areas of probability and distribution theory, discrete and continuous random variables and joint distributional behaviour, as well as introducing principles of likelihood theory, estimation, confidence intervals and hypothesis tests. In addition, topics such as moment and cumulant generating functions are introduced, as well as an introduction to random sums and Central Limit Theorem based large-sample distributional approximations.
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ACSC71-306: Stochastic Processes (10CP)
The 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.
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ACSC71-307: Survival Analysis (10CP)
The 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.
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DTSC71-300: Infrastructure for Data Analytics (10CP)
Using 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.
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DTSC71-301: Deep Learning Through Neural Networks (10CP)
Computer vision, natural language processing and personalised recommendations are just a few of the uses of artificial neural networks that are increasingly relevant to real-world problems that pose challenges for traditional data analysis techniques. This subject introduces students to the foundational ideas associated with the many variations of these models that have been developed for domains involving image data, temporal data, and natural language. This includes feed-forward, fully connected neural networks, convolutional neural networks, recurrent neural networks, and the transformer architecture. Class discussions will introduce the technical underpinnings of the models and applied sessions and assessments provide students the opportunity to experiment and apply them to a wide range of practical, real-world problems using Python.
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DTSC71-302: Statistical Learning and Regression Models (10CP)
This 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.
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DTSC71-306: Modern Machine Learning Models (10CP)
This 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.
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DTSC71-307: Advanced Statistical Learning Models (10CP)
This subject extends the investigation of modern statistical modelling techniques initiated in Statistical Learning and Regression Models. Topics include models for correlated data including spatial and mixed-effects models, as well as Bayesian hierarchical models including discussion of Markov chain Monte Carlo (MCMC) techniques for calculating posterior estimates, and modern applied re-sampling methods for developing robust measures of model accuracy. The programming language R will be used in this subject.
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ECON71-300: Advanced Econometrics (10CP)
Many 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.
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General Elective
Students must choose ten credit points (10CP) of a postgraduate subject from across the University.
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University Postgraduate Electives
Students may choose from all postgraduate subjects across the University that are available as general electives.
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Study plan
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.
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