Bachelor of Data Analytics (3 Year Program)

In this subject, students will be introduced to critical thinking and clear expression. They will evaluate arguments, identify assumptions, judge patterns of inference, and recognise and apply various methods of reasoning. Students will learn how to clarify and visually represent their thinking to make better decisions, evaluate and use evidence, and communicate more effectively in their writing and speaking. Using these skills, students will structure and write an academic essay and deliver an oral presentation.

Integrity, and the courage and capability to act on one’s sense of responsibility, are key components of a thriving life. Responsibility, Integrity and Civic Discourse fosters students’ lifelong commitment to responsible discourse and action in all spheres of human interaction, recognising the global aspect to contemporary citizenship. Students explore the complex relationship between character, responsible action, and creative critical thinking, learning how to reflect on and articulate their unique sense of global citizenship and responsibility. By accentuating the importance of justification and articulation of the reasons for our actions, students exercise their critical, communicative, and cooperative capabilities so that they can thrive with integrity in the multiple contexts of action they will face as private, civic, professional, and global citizens.

In today's work and study environments, individuals often encounter complex, open-ended problems that necessitate collaboration in both physical and virtual realms and across sectors and specialisations. In Collaboration for Global Change, students engage collaboratively to craft genuine solutions for global issues. In this context, students link their endeavours to specific sustainable development goals, thus positioning their actions as contributions to global citizenship. As they learn to defend their ideas and perspectives, students apply critical thinking, design thinking, problem-solving, and communication skills within a problem-based learning environment. This comprehensive approach equips them with the necessary skills and mindset to excel in future work, academic pursuits, and global initiatives.

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.

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.

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.

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.

The theory and practice of advanced regression techniques is the focus of this subject. Topics such as regularisation, limited dependent variable models, generalised linear models, random and mixed effects models, splines,  additive models and tree-based regression will be covered. The programming language R will be used in this course.

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.

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.

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, generalised linear models and their applications. 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.

Designed to foster the development of foundational mathematical and statistical skills necessary for subsequent quantitative subjects in the Bond Business School. This includes applications of calculus, probability, discrete and continuous random variables, sampling distributions, hypothesis testing, and application of the central limit theorem to large sample inference and data analytics. The use of popular statistical computing packages is integral to providing an applied approach to these topics.

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.

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.

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.

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. 

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.

This subject extends the investigation of modern statistical modelling techniques initiated in Statistical Learning and Regression Models. Topics range from modern applied resampling/bootstrap methods to develop robust measures of model accuracy, to handling correlated data with mixed-effects models, and Bayesian Networks. Analysis will be conducted in programming languages such as R and Python.

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.

A minor in economic modelling and analysis sets you up to take on a range of roles in business and public policy analysis. In addition to basic micro- and macroeconomic theories, you will develop econometric skills which allow you to make sense of economic trends and inter-relationships which will make you a valuable member of any economic analysis team.

A minor in health system analytics provides a solid background in the important drivers of the health system. You will develop skills to aid health network administrators increase efficiency, understand population medical trends and assess potential clinical interventions.

A minor in market analytics provides a detailed background into the theory and practice of quantitative aspects of marketing and advertising. You will develop skills in monitoring trends in consumer sentiment and purchase patterns as well as targeting campaigns to optimise marketing resources and increase sales.

A minor in psychometrics provides a grounding in the experimental design and analysis principles employed in psychological research. You will develop the skills necessary to be an important part of the planning and analysis of psychology and other allied health related quantitative research and testing.

A minor in quantitative finance provides training in the understanding of market trends, accurate pricing of finanical instruments and modelling of business cycles. You will develop skills in econometric and time series analysis that will allow you to understand and investigate a range of investment and hedging strategies for the purpose of wealth creation.

A minor in sport analytics will develop key skills in the management, promotion and understanding of the role of sport in society. You will investigate both professional level sporting activities and grass-roots level sporting organisations and their importance to local culture.