Bachelor of Business 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 subject provides a broad and important foundation in key accounting concepts, tools, and processes of the discipline. Students will learn to apply fundamental accounting concepts and procedures to prepare and interpret basic financial statements for different types of business entities. Both manual and computerised accounting systems are used to demonstrate the accounting process. Users of financial information and the decisions they make based on accounting information are considered throughout the subject. 

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.

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.

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.

Students are introduced to economic analysis and its applications. Topics include: decision making, analysis of constraints, analysis of benefits and costs, maximisation, competitive pressures and market forces, and public policy issues. By the end of the subject, successful students will be equipped with the tools of economics and prepared to address economic problems in their day to day lives, industry, politics, society, and the environment.

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.

This subject introduces the analytical approaches used by managers when making financial decisions. The theory and application of fundamental concepts of time value of money, the relationship between risk and return (i.e., CAPM), portfolio theory of investment (i.e., diversification), and capital structure are examined in detail. Students will apply these concepts and assorted financial tools to value stocks and bonds, estimate the cost of capital and implement the discounted cashflow technique to make capital budgeting decisions. Students will also gain exposure to real-time market data via the Bloomberg database.

Marketing is the art and science of understanding and satisfying customer needs by creating value for both the customer and the marketer. Facilitating these exchanges requires several interrelated processes and activities including market research, market segmentation, product, pricing, promotional strategies and distribution. In this introductory subject, you will explore the various theories and frameworks of marketing and learn to apply them to marketing phenomena around you. 

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 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.

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 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.

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 accounting from Bond University sets you up to take on roles in a wide range of interesting and rewarding careers. You will find that your accounting skills will be useful whether you aspire to work for a government, NGO, charity, university, any type or size of business; and if your aim is to own or start up your own business, these skills will be especially relevant. Taking our accounting minor is an astute way to complement your studies in other fields of business.

Economics is a social science that uses the scientific method of enquiry in its approach to understanding how societies, governments, businesses, households, and individuals allocate their scarce resources. An undergraduate minor in economics focuses on learning how to understand the world in terms of trade-offs and incentives. An economics minor assists in learning how to analyse data as well as the development of clear-headed logic. It involves the use of mathematical and statistical tools whenever possible to assist in this analysis.

This minor will appeal to those enterprising individuals with an eye towards their own start-up business, or who are keen to work as a “intrepreneur” inside an organisation. Students will gain skills in ideation, creativity and innovation in planning a business and developing a viable business model. Through experiential learning and support from industry experts, students will graduate with contemporary and best practice knowledge, insights, and strategies with which to implement effective management practices and bring a business idea into fruition.

A minor in finance from Bond University sets you up to take on roles in a wide range of interesting and rewarding careers. You will find that your finance skills will be useful wherever you aspire to work and could lead to interesting and rewarding positions in business, banking, investment, and government. The scope of subjects for this minor covers personal, corporate and international finance plus another subject of your own choice. Taking our finance minor is an astute way to complement your studies in other fields of business.

This minor provides students with a wide body of knowledge, skills and strategies that are critical to long-term organisational success. The scope of subjects covered in the Management Minor provides students with the ability to identify strategic problems and develop viable solutions for a range of organisational issues.

This minor develops students’ knowledge, skills, and practical experience in designing and implementing market-driven, value-creation marketing strategies.