Subjects overview
This program can be completed in 1 year 4 months (4 semesters)
Required Subjects
Students must complete the following sixty credit points (60CP) of subjects.
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DTSC71-100: Business Analytics Coding (10CP)
This subject is an introduction to programming. There is a focus on writing computer code to solve problems in business, which promotes the development of problem-solving skills. The necessary foundation concepts are covered, including expressions, variables, data structures, control structures, functions, commenting and debugging. Although it can be taken as a stand-alone subject, it is specifically designed for students interested in future study in data science and big data analytics. Two widely popular programming languages for data science, R and Python, will be used as vehicles for learning programming. Cutting-edge R and Python packages used by data scientists will be covered in this subject. Prior coding knowledge and experience is not a requirement for this subject
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DTSC71-200: Data Science (10CP)
Organisations use their data for decision support and to build data-intensive products and services. The collection of skills required by organisations to support these functions has been grouped under the term Data Science. This subject will articulate the expected output of data scientists and then equip students with the ability to deliver against these expectations. A particular focus will be given to the tools required to model, store, clean, manipulate, and ultimately extract information out of stored 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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ECON71-200: Linear Models and Applied Econometrics (10CP)
Econometrics is a sub-discipline of both statistics and economics and presents one interface between statistical theory and the real world. It provides the tools with which to test hypotheses and to generate forecasts of business activity. Topics include the classical regression model, remedial measures for violation of regression assumptions, binary choice models, panel data models and their applications. The technique such as hypothesis testing and its application will allow students to specialise in areas such as market research and other disciplines. The skills that students will develop in this subject are crucial in any applied work and will constitute an essential ingredient in most jobs in the field of business application, whether in the public or private sector.
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Alternate Choice Analytics
Students must choose forty credit points (40CP) of the following subjects.
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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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DTSC71-110: 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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DTSC71-303: Data Analytics Case Studies (10CP)
Data analysis is the skill of the future. This subject identifies and critically analyses the concepts, theories and frameworks of big data research in a series of case studies. Through analysis and critique, students will develop an expert understanding of the research process and how the research presented in each case study has been relevant to both industry and the academic community. Students will synthesise the relevant literature, identify the big data techniques and interpret the results and evaluate their implications.
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DTSC71-304: Applied Data Analytics Project (10CP)
Knowing how to understand, analyse and present data is a key to entry in any industry. This subject requires students to apply the concepts, theories and frameworks from their entire program to a big data research project. Working under the supervision of an academic staff member, students will apply the research process, develop a research question that is relevant to both industry and the academic community, synthesise the relevant literature, use appropriate big data techniques and interpret the results and evaluate their implications. Students may work in teams or as individuals dependant on the size of the project. Projects may be created internally or be sourced from industry.
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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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DTSC71-306: Advanced Machine Learning (10CP)
This subject is designed to provide students with the knowledge and skills to develop applications of Deep Learning to Big Data in a modern business setting. Specifically, students will learn how Deep Learning models extract complex abstractions as data representations through a hierarchical learning process to learn and infer from Big Data datasets. Students will study how a key benefit of Deep Learning, the analysis and learning of massive amounts of unsupervised data, makes it a valuable tool for Big Data Analytics. The subject finishes with an investigation into the latest research being undertaken involving Deep Learning models.
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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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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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Alternate Choice Business
Students must choose ten credit points (10CP) of the following subjects.
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ACCT71-100: Accounting Principles (10CP)
This subject introduces students to accounting concepts, procedures, and influences under which basic financial statements are prepared, and focuses on how financial and business information is used for decision-making by various stakeholders. Students are also introduced to concepts of governance, social responsibility, business ethics, and the ethical standards expected of accountants, and they will integrate this knowledge to make decisions and solve problems in a range of complex, contemporary business situations. The use of both manual and computerised accounting systems is explored, and the use of other contemporary business software platforms will develop technology skills which are fundamental to accounting practice.
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ECON71-100: Principles of Economics (10CP)
An introduction to economics for postgraduate students, with a focus on microeconomics. An overview of fundamental topics such as opportunity cost, trade-off, relative scarcity and marginal analysis are explained using contemporary issues including minimum wages, carbon taxes, competition policy and state ownership of monopolies. The underlying structure of macroeconomics is also included to provide a framework for understanding the economy as a whole and the news and policy that affects it.
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FINC71-101: Fundamentals of Finance (10CP)
This subject introduces the analytical approaches used by managers when making financial decisions. Core topics include the time value of money, the relationship between risk and return (i.e., CAPM), portfolio theory (i.e., diversification), and capital structure. On successful completion of the subject, students will be able to apply these concepts to value both stocks and bonds, estimate the cost of capital and implement discounted cash flow techniques in order to make capital budgeting decisions. Students will also gain exposure to real-time market data via the Bloomberg database.
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MKTG71-600: Marketing Fundamentals (10CP)
An introduction to the essentials of marketing critical to managing profitable customer relationships in today’s dynamic and connected environment. You will learn how to acquire and retain the right customers through the application of consumer behaviour, market research, market segmentation, targeting, positioning and customer relationship management. The primary aim of this subject is to foster a customer-centric orientation and a marketing mindset when addressing business issues.
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General Electives
Students must choose ten credit points (10CP) of postgraduate subjects 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.
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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