Subjects overview
This program can be completed in 1 year 4 months (4 semesters)
Required Subjects
Students must complete the following seventy credit points (70CP) 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)
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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DTSC71-304: 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. Projects may be created internally or be sourced from industry.
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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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General Electives
Students must choose fifty credit points (50CP) 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.
Opportunities
Students may take advantage of the following opportunities.
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Postgraduate Opportunities
Students may have the opportunity to participate in an international study tour experience or internship as a general elective. Those interested should consult with an Enrolment Officer in Student Assist for guidance and to check eligibility requirements (e.g., GPA, language proficiency, prerequisites). Students should make informed decisions and ensure their chosen international experience or internship aligns with their academic and personal goals.
Participating in such an opportunity may involve additional costs, which may vary depending on the opportunity's location, duration, and nature. Students are responsible for all associated expenses, including travel, accommodation, visa fees, insurance, and any program or placement fees that may be applicable.
Opportunities
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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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