Subjects overview
This program can be completed in 92 weeks
Required Subjects
Students must complete the following eighty credit points (80CP) of subjects.
-
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.
Read more -
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.
Read more -
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.
Read more -
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.
Read more -
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.
Read more -
DTSC71-306: Modern Machine Learning Models (10CP)
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.
Read more -
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.
Read more -
STAT71-112: Quantitative Methods (10CP)
This subject provides the opportunity to develop the 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. Popular statistical computing packages are used as an integral part of the subject to provide an applied focus throughout the subject.
Read more
Professional Option
Students must choose forty-five credit points (45CP) of the following subjects. BUSN71-701 Professional Portfolio (OR) BUSN71-705 Professional Development.
-
BUSN71-701: Professional Portfolio (45CP)
Professional Portfolio is a 20 week, 45 credit point subject available to Masters (Professional) students in the Bond Business School. This subject blends both practical and theoretical components to complement your program of study by enhancing your employability and professional skills. A key element of this subject is a structured and supervised 210-hour vocational experience that is tailored to address placement objectives mutually agreed upon between each student, industry partner and the instructor. Combined with individualised mentoring sessions with the instructor, this subject is designed to enable students to develop their professional skills in a real-world environment.
Read more -
BUSN71-705: Professional Development (45CP)
Professional Development is a 20 week, 45 credit point subject available to Masters (Professional) students in the Bond Business School. The subject blends both practical and theoretical components to complement the program of study by enhancing employability and professional skills. This includes providing a variety of tools to develop a flexible career strategy and ultimately communicate the students’ professional brand to potential employers. Students will also complete an individual research project exploring an industry and/or organisation that is the target of their professional aspirations.
Read more
Analytics Option
Students must choose twenty credit points (20CP) of the following Analytic Option subjects.
-
ACSC71-200: Mathematical Statistics (10CP)
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.
Read more -
ACSC71-306: Stochastic Processes (10CP)
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.
Read more -
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.
Read more -
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
Read more -
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.
Read more -
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.
Read more
Applied Options
Students must choose twenty credit points (20CP) of the following Applied Option subjects.
-
ACCT71-211: Accounting Information Systems (10CP)
Information technology is an essential accounting tool. Amongst other applications, it is used to automate transactions and business processes, streamline reporting and support business analysis. This subject covers the critical evaluation and design of accounting information systems (AIS) and their use in complex managerial decision-making. It provides both a theoretical and practical understanding of AIS in a broader industry and corporate setting. Emphasis is placed on the integrated nature of AIS and technology, control mechanisms, data analysis and reporting. The applied nature of the subject enables the development of advanced practical skills in using accounting software for transaction processing and decision support.
Read more -
ACCT71-306: Data Analytics for Accountants (10CP)
Data analytic skills are core for accounting practitioners in our data-intensive business environment. In this subject, we build on advanced data and analysis concepts to develop an analytical mindset where students learn to frame complex managerial questions, assemble the data, compute relevant metrics and models, identify actionable insights, and design effective and efficient communication of the outcomes. Students explore and apply these skills in a variety of contexts, including management accounting, tax, audit and assurance, and financial statement analytics to develop practical skills in working with multiple analytics tools and develop skills in critically evaluating which tool is best suited for a particular problem or question. Finally, students will explore how to best interpret and communicate the results from data analytic procedures using visualisations.
Read more -
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.
Read more -
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.
Read more -
HPER71-110: Evidence Based Practice and Policy (10CP)
As society grapples with the increasing demand and spiralling costs of healthcare and unexplained variations in practice, the delivery of healthcare based on evidence has never been more important. The growing volume of this evidence presents significant challenges for health professionals. After exploring the preconceptions of evidence based practice, challenging and evaluating the theory behind it, participants in this subject will develop the skills needed to efficiently access and examine evidence that supports and affects healthcare practice. Participants will be able to develop searchable questions from practice and policy problems, understand the type of research studies that can address these questions, and recognise the strengths and limitations of the different research study designs. Skills in locating research evidence and critically evaluating the evidence will be developed and refined through supported hands-on practice. Participants will be able to interpret and effectively communicate the findings of research evidence to different audiences and make evidence-informed decisions to support clinical practice and healthcare delivery.
Read more -
HPER71-119: Leading Innovation in Healthcare (10CP)
A key challenge for contemporary leaders in healthcare is to navigate change thoughtfully. Innovation in healthcare encompasses the introduction of new ideas, products, or services that add value to important problems in healthcare systems. This subject introduces a continuum of change from improvement through innovation towards transformation, which is driven by different levels of complexity and uncertainty within healthcare organisations. While both leading improvement and leading innovation are essential, they differ in terms of scope, approach, and outcomes. Effective leaders in healthcare need to balance both aspects, recognising the need for continuous improvement while also fostering a culture of innovation to stay ahead in a rapidly evolving healthcare landscape. Strategies for managing incremental improvement will be highlighted from change management theories and current practice. The role and potential benefit of innovation in complex healthcare systems will be explored. Strategies to lead, support, and implement innovation will be introduced. Learners will develop the capability to determine the most appropriate strategies to lead change across the continuum from improvement to innovation. Learners will also be guided to propose a plan to lead innovation in their chosen workplace.
Read more -
MKTG71-303: 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.
Read more -
MKTG71-315: Marketing Analytics (10CP)
This subject enables students to apply appropriate marketing analytical tools and techniques to address a range of marketing challenges. Importantly, it will also highlight the role and importance of marketing analytics to support a data-driven approach to marketing strategy and decision making. Through a variety of applied activities and assignments, students will have the opportunity to develop a thorough conceptual understanding of marketing analytics and its applications.
Read more -
PSYC71-409: Multivariate Research Methods (10CP)
This subject introduces multivariate research design and multivariate analytic techniques, the use of statistical packages such as SPSS, and the interpretation of results. Multivariate procedures include multiple regression analysis, discriminant function analysis, factor analysis, and structural equation modelling.
Read more
Opportunities
Students may take advantage of the following opportunities.
-
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
Major / Minors / Specialisations Finder
Oops, something went wrong.
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.
Discover
Experience Bond for yourself
Connect
Speak to a current Bond student
Program not available for domestic students
Your preference is currently set to domestic. If you’re an international student, return to the program.