Graduate Certificate in Business Analytics

Business Analytics can be defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions (Davenport et al. 2010). Organizations recognize the need to learn more about business analytics as high quality information about their capabilities and those of their competition is essential for effective decision making to be competitive and generate value.

This course presents the fundamentals of implementing and managing business analytics in organizations including: decision making; business analytics concepts and frameworks; technologies and tools required for descriptive analytics, predictive analytics, and prescriptive analytics; frameworks for putting analytics to work; technologies and tools required for business analytics; the governance, oversight and business value gained from business analytics within organizations; ethical and social implications of business analytics; future directions for business analytics.

SAS software tools will be used for the skills components of the course: “Applied Analytics Using SAS Enterprise Miner”.

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Today’s organisations operate on a global scale and collect huge volumes of data from multiple sources. Management recognises the need to learn how to gather and manage strategic data, transform it into new insights, and translate those insights into effective front-line action in order to have a better understanding of business performance and gain competitive advantage.

This course will provide students with advanced business analytics methods including predictive analytics and Visual analytics. Both meaningful hands-on experience and case studies describing organisational experiences with business analytics are included. The course has a technical component in which students gain practical knowledge and skills in data mining and visual analytics.

SAS software tools will be used for the skills components of the course: “Customer Segmentation Using SAS Enterprise Miner” and “SAS Visual Analytics: Getting Started”

UNSW Online Handbook Summary
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UNSW Online Handbook Summary
UNSW Class Timetable

​This course provides an introduction to basic analytical skills. It provides a solid basis from which data analysis techniques and tools can be applied to solve business problems. There is an emphasis on problem solving and business analytics by both manual and digital methods. The first half of the course will focus on the use of quantitative methods and techniques. The second half of the course will focus on the use of qualitative research methods and techniques.

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This course introduces leading edge systems thinking tools to enhance your ability to diagnose and solve complex business and societal problems. You will learn a flexible and powerful approach to structuring managerial problems and visualising the interconnectedness of business, social and environmental systems. You will develop skills to conceptualise and build simulation models of an enterprise, enabling you to explore the dynamic consequences of different strategy/policy decisions and identify key leverage points in the system. You will also discover how to elicit team knowledge to achieve a shared mental model among managers and/or policy makers, converting a simulation model into a Management Gaming Simulator. In the course, students explore a broad application of business and public policy issues including firm growth and stagnation, competitive strategy, capability development and human resource policies, and the boom and bust dynamics of start-up enterprises. Industries discussed include international oil production, passenger airlines, real estate, global wine wars, cyclical commodity markets, and also examples from public policy and social enterprises. In addition, students examine cases of organisations that have successfully applied systems thinking and business dynamics.

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This course covers the practical use of econometric and statistical techniques applied in the business world. Building and evaluating long term forecasting models based on econometric techniques using  time series data will be the focus of this course. Understanding these issues will allow students to formulate and use the reliable forecasting models based on sound economic principles.

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Performing business analytics functions on huge volumes of data generated from various day-to-day operations requires a solid understanding of how data captured at the operational level can be re-structured and used at the enterprise level to gain maximum effective results.

This course will provide students with data management knowledge in both operational and analytical database systems and infrastructure aspects, including theoretical foundations as well as meaningful hands-on experience. Examples include: standard relational database issues; analytical database topics; requirements imposed by the move to big data; business analytics infrastructure and tools used with big data.

SAS software tools will be used for the skills components of the course: “SAS SQL 1: Essentials” and “Introduction to SAS and Hadoop”

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Businesses are confronted with a world in which customers are increasingly using social media/technologies as central means of communication, collaboration, social exchange, and navigation of the Internet. This course develops the ability to use social technologies in business contexts. This course aims to enable students to analyse and develop strategies related to the use of social technologies within businesses and the use of social media as for external relations (especially with customers). Furthermore, the course introduces students to the underlying theoretical concepts of social media as well as hands-on experience with current social technologies relevant for business.

The teaching includes working on several real-life cases and employs a range of social technologies. On successful completion of the course, students are expected be able to demonstrate both a professional perspective as well as technological confidence regarding social media and their business use.

SAS software tools will be used for the skills components of the course: “Text Analytics Using SAS Text Miner”

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There is an increasing need for the use of data analysis that can deal with large and fast flowing data. The focus of the new course will be on learning techniques that will allow marketing managers to provide insights into consumer decision making utilising market-basket analysis, optimisation techniques, social media analysis and web-analytics.

UNSW Online Handbook Summary
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UNSW Online Handbook Summary
UNSW Class Timetable

The course reviews key concepts of statistics and looks at a number of data sources used to analyse risks in various disciplines. Applications such as forecasting, modelling extreme events and dependencies are illustrated through their implementation in a number of practical problems. These are aimed at making students aware of the power of statistics in quantitative risk analysis, and its areas of applicability. Traditional risk measures and models routinely used to analyse financial, insurance, environmental, health and safety, engineering reliability and security risks data are reviewed. Emphasis is placed on the use of these tools in practical applications, which is achieved through the presentation of real-life examples. Simulation is discussed as a tool to analyse risks in complex systems, where data is not available.

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This course aims to introduce students to the field of decision analysis. At the end of the course students should be able to develop solutions to basic decision problems involving uncertainty, using specialised, free-ware software. An intuitive exposition into Bayes’ theorem is given through a number of simple-to-follow problems involving information update in light of new evidence, to show the importance of this theorem in making decisions about risk. Students learn about subjective probabilities and various formal methods of expert opinion elicitation. Two specific decision tools - Bayesian networks and Multi-criteria Decision Analysis (MCDA), are examined in detail.

Bayesian networks, which display graphically the interrelationships between variables, allow for their dependencies to be accounted for in a manner reflecting their causal relationships. This permits the evaluation of predicted consequences associated with different interventions, before these are actually implemented. MCDA allows decisions to be made when the decision-maker is faced with multiple and conflicting objectives where trade-offs must be made, which is most often the case in the real world. Netica and SuperDecisions are free-ware software implementing Bayesian networks and MCDA, respectively, which students will use during the course to solve decision problems from a range of application fields.

Finally the course will discuss current issues in behavioural risk management and the value of first-line employees’ experience, such as their ability to identify ‘early warnings’ about risk, and what an organisation can do to encourage such employees to speak up without the fear of retribution. The aim is to demonstrate the benefits of front-line employees taking charge of local risk management issues and the importance role risk culture plays in allowing this to occur.

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