Subjects overview
This program can be completed in 4 months (1 semester)
Analytics Electives
Students must choose twenty credit points (20CP) of subjects from the following electives.
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DTSC71-110: Cyber and Fraud Threats in Organisations (10CP)
All 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.
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DTSC71-300: Infrastructure for Data Analytics (10CP)
Building on students’ existing knowledge of data science techniques, this subject investigates the range of deployment options to automatically extract insights from the vast amount of data available. This includes traditional server and database deployment, as well as a range of popular cloud solutions including open-source alternatives. The advantages and disadvantages of different approaches will be discussed. In addition to popular big data analytics deployment options such as Amazon Web Services (AWS), Microsoft Azure, Google Big Query, Apache Spark, H20.ai and NoSQL, students will also learn about the MapReduce and Hadoop framework. Importantly, security implications associated with big data analytic deployments will be discussed, including knowledge of principles for cybersecurity and an ability to implement basic best practices.
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DTSC71-301: Applied Machine Learning (10CP)
Unprecedented 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.
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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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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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Financial Electives
Students must choose twenty credit points (20CP) of subjects from the following electives.
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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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DTSC71-305: Financial Trading Systems (10CP)
This 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.
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FINC71-302: Finance Applications and Analysis (10CP)
An introduction to statistical techniques used in financial analysis and decision-making. Specific applications include capital budgeting, capital asset pricing model, arbitrage-pricing, portfolio modelling and the study of co-movements of different financial assets. The use of spreadsheets and related software tools is central to the learning experience of this subject to provide extensive opportunities to develop practical skills in financial analysis and modelling.
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MKTG71-602: Market Research (10CP)
Marketing 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.
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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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