Advanced Business Intelligence and Analytics

Postgraduate | 2026

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Area/Catalogue
INFO 5019
Course ID icon
Course ID
203951
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
5
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
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Note:
Course data is interim and subject to change

Course overview

The aim of this course is to apply business intelligence (BI) and analytics frameworks and methods to support reporting and decision making. Students in this course will apply CRISP-DM framework to support reporting and decision-making on the given business task. The course content covers understanding of the business task and selection of suitable data for analysis, data quality assessment, preliminary exploration of the problem and writing analytic plan; application of analytic methods to build the solution (model), critical evaluation of the solution against the business requirements, and summarising results with justifications into a report for the decision-makers. Students are introduced to Tableau and KNIME tools (but use other suitable tools is negotiable for assignments), and will be introduced into ethical and legal aspects of data collection, access, storage, and analytics.

Course learning outcomes

  • Explain the nature and role of business intelligence and analytics in decision-making, actionable insights, and contributing to the delivery of business value and competitive advantage in modern organisations.
  • Apply the CRISP-DM framework in a realistic analytic task.
  • Compare and evaluate key data analytics methods, be able to discuss the pros and cons of those methods; apply this knowledge in writing analytic plan.
  • Apply analytic methods, build models, and critically evaluate the model against the given business task.
  • Summarise the results and recommendations in a report and defend the report in discussion.
  • Use modern analytics software tools to solve real world problems.

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A