Provides students with the necessary knowledge and skills to effectively use business analytics to support decision making in the context of ‘big data’ as a strategic resource. As a ‘data-driven’ unit, students will be exposed to a variety of analytical techniques using software applications.
Topic 1: Introduction to business analytics
Topic 2: Data visualisation
Topic 3: Dimension reduction
Topic 4: Evaluation of predictive performance
Topic 5: Multiple linear regression
Topic 6: k-Nearest Neighbours (k-NN)
Topic 7: Logistic regression
Topic 8: Association rules and collaborative filtering
Topic 9: Cluster analysis
Topic 10: Forecasting - Part I
Topic 11: Forecasting - Part II
Topic 12: Revision
Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.
Learning outcomes and graduate attributes
|On completion of this unit, students should be able to:||GA1||GA2||GA3||GA4||GA5||GA6||GA7|
|1||Appraise the role of data analytics and implications of big data in helping organisations identify new opportunities and turn big data into a strategic resource.||Knowledge of a discipline||Communication and social skills|
|2||Identify patterns and trends for transformation of big data into meaningful information for use to gain competitive advantage.||Intellectual rigour||Knowledge of a discipline|
|3||Apply statistical techniques using industry software to analyse relationships in the data for forecasting and evaluation purposes.||Knowledge of a discipline|
|4||Evaluate and communicate results of analysis in a framework for translating data analysis into decision-making outcomes in a variety of business settings.||Intellectual rigour||Communication and social skills|
- An e-book of the following is also available (ISBN: 978-1-118-87933-7): Shmueli, G, Bruce, PC, Yahav, I, Patel, NR & Lichtendahl, KC Jr, 2017, Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, Wiley. ISBN: 978-1-118-87936-8.
Teaching and assessment
|Lecture online 1 hour (12 weeks)|
|Short written response||20%|
|Short written response||30%|
Commonwealth Supported courses
For information regarding Student Contribution Amounts please visit the Student Contribution Amounts.
Commencing 2018 Commonwealth Supported only. Student contribution band: 3
Please check the international course and fee list to determine the relevant fees.