Unit rationale, description and aim

Organisations today face overwhelming amounts of data, organisational complexity, rapidly changing customer behaviour, and increased competitive pressures. New technologies as well as rapidly proliferating channels and platforms have created a massively complex environment. At the same time, the explosion in data (so-called ‘big data’) and digital technologies has opened up an unprecedented array of potential insights into customer needs and behaviour.

Data analytics refers to a range of computational and statistical techniques used to extract ‘meaning’ (i.e. comprehensible and useable information) from large, variable, undifferentiated and volatile raw data sets. These techniques transform, organise and model the data to draw conclusions and identify patterns of activity that enable businesses to make more-informed decisions about mission-critical activities such as revenue sources, operational efficiencies, markets and supply chains.

The primary aim of this unit is to achieve an understanding of the principles, major techniques and applications of data analytics as a managerial decision-making toolset and foundation for evidence-based business practices. 

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

Nil

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Demonstrate an understanding of the value of data ...

Learning Outcome 01

Demonstrate an understanding of the value of data collection and analysis in contemporary business decision making through the engagement and participation of stakeholders

Collaboratively analyse explicit and implicit requ...

Learning Outcome 02

Collaboratively analyse explicit and implicit requirements for carrying out a data analysis task to meet stakeholder purposes, providing the evidentiary justification for a business decision

Assess and schematize the technical issues present...

Learning Outcome 03

Assess and schematize the technical issues present in the stages of a data analysis task and the properties of different technologies and tools that can be used to deal with the issues

Evaluate and select analytical techniques appropri...

Learning Outcome 04

Evaluate and select analytical techniques appropriate for evaluation of a predictive model based on data analysis, and justify their choice

Analyse and explain the issues and challenges asso...

Learning Outcome 05

Analyse and explain the issues and challenges associated with interpreting data sets for organisational decision making mindful of the rights and responsibilities of stakeholders

Content

Topics will include:

  • data description and exploration
  • evidence-based business decision making
  • identify, interpret and analyse stakeholder needs
  • information quality and integrity
  • statistical and decision-making techniques in business
  • the selection and application of appropriate tools for data visualization and presentation
  • scenario modelling for decision making
  • data analytics, stakeholder participation, and project management
  • apply systems thinking to understand complex system behaviour including interactions between components and with other systems, including social, cultural, legislative, environmental and business systems
  • identify and apply relevant problem-solving methodologies mindful of the rights and responsibilities of stakeholders
  • prediction and forecasting

Assessment strategy and rationale

The assessment strategy for this unit for both multi-mode and online delivery is based on the need to determine authentic student achievement of the learning outcomes. The first assessment consists of short answer questions to assess students’ understanding of the basic concepts of data analytics and decision making and its importance to organizations. The second assessment provides students with an opportunity to engage in a simulated professional activity and to apply learning and skills acquired in the first assessment. The use of case studies in the second and third assessments promote synthesis of what has been learned across a range of topics, and will enhance and consolidate the learning in the Business Report Project.

Overview of assessments

Quiz on concepts This assessment is to test stu...

Quiz on concepts

This assessment is to test students’ understanding of the fundamental concepts data analytics and decision making and its importance to organizations. The quiz will contain short answer questions. The feedback from this assessment will help students to be ready to apply the concepts in the next assessments.

Weighting

20%

Learning Outcomes LO1

Case Study Analysis (Group task) The primary pur...

Case Study Analysis (Group task)

The primary purpose of this assessment is to provide students with an opportunity to analyse business data in a managerial context. In addition to assessing knowledge acquisition, the aim of the assessment is to provide students with feedback on their understanding of relevant concepts and techniques and to prepare them for the third assessment. 

Weighting

30%

Learning Outcomes LO2, LO3

Business Report Project This assessment is desi...

Business Report Project

This assessment is designed to develop students’ skills in the correct usage of analytical techniques and interpreting data for making managerial decisions. The main task is to analyse business data and to prepare a report for management based on an analysis of the data. The focus is on understanding the use of data analytical tools in a business context. 

Weighting

50%

Learning Outcomes LO4, LO5

Learning and teaching strategy and rationale

Multi-mode

In multi-mode, the unit will be delivered over a twelve-week semester or equivalent study period. Students will have access to all primary learning materials online, along with formative and summative assessments, all of which will be available in LEO, so as to provide a learning experience beyond the classroom. While there are no formal classroom lectures for this unit, students taking the unit in multi-mode will be required to attend weekly two-hour workshops, which will include a seminar and specific tasks related to achievement of the unit learning outcomes. Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.

Online

In online mode, the unit will be delivered over a twelve-week semester or equivalent study period. None of the assessments involves group work. Students will have access to all primary learning materials online, along with formative and summative assessments, all of which will be available in LEO. In online mode, there are no lectures or workshops, and all seminar and simulated workshop activities will be conducted online Students should anticipate undertaking 150 hours of study for this unit, including readings, online forum participation and assessment preparation.

Representative texts and references

Representative texts and references

Albright, SC & Winston, WL 2016, Business analytics: data analysis and decision making, 6th edn, South-Western College, Mason, OH.

Barton, D 2016, Data analytics: a comprehensive beginner’s guide, CreateSpace, San Francisco, CA.

Cady, F 2017, The data science handbook, Wiley, Hoboken, NJ.

Franks, B 2014, The analytics revolution: how to improve your business by making analytics operational in the big data era, Wiley, Hoboken, NJ.

Haider, M 2015, Getting started with data science: making sense of data with analytics, Pearson Higher Education, Upper Saddle River, NJ.

Kennett, RS & Shmueli, G 2016, Information quality: the potential of data and analytics to generate knowledge, Wiley, Hoboken, NJ.

Kinley, P 2016, Data analytics: a basic guide to master data analytics, CreateSpace, San Francisco, CA.

Lin, M 2015, Applied business analytics: integrating business process, big data and advanced analytics, Pearson FT Press, London.

Mayer-Schonberger, V & Cukier, K 2014, Big data: a revolution that will transform how we live, work and think, John Murray Publishers, London.

Vollenweider, M 2017, Mind+machine: a decision model for optimizing and implementing analytics, Wiley, Hoboken, NJ.

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