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Data Analytics for Business

Unit of study table (Table A)

Achievement of a specialisation in Data analytics for business requires the completion of:

(a) 6 credit points of Foundational units of study in the same area as the specialisation; and*
(b) 24 credit points of Data analytics for business specialisation area units of study comprising:
(i) 6 credit points of Data analytics for business core units of study; and
(ii) 18 credit points of Data analytics for business selective units of study.
Students completing this specialisation to meet the requirements for the Master of Commerce or as their compulsory specialisation for the Master of Commerce (Extension) must complete a 6 credit point capstone unit related to the specialisation from Table A - Capstone units of study.
Students completing this specialisation as an optional second specialisation for the Master of Commerce (Extension) do not need to complete this capstone unit.

 

Unit of study Credit points A: Assumed knowledge P: Prerequisites
C: Corequisites N: Prohibition

(a) Foundational unit of study*

* Note. Foundational units count towards both the Foundational units of study for the course and the specialisation.
QBUS5001
Foundation in Data Analytics for Business
6 A Students should be capable of reading data in tabulated form and working with Microsoft EXCEL and doing High School level of mathematics
N ECMT5001 or QBUS5002

(b) Data analytics for business

(i) Core units of study
BUSS6002
Data Science in Business
6 A Basic knowledge of probability and statistics
C QBUS5001 or QBUS5002
(ii) Selective units of study
CSYS5040
Criticality in Dynamical Systems
6 A Mathematics at first-year undergraduate level. Some familiarity with mathematical and computational principles at an undergraduate university level (for example, differential calculus or linear algebra). Familiarity with a programming language at a beginners level for data analysis
INFS6018
Managing with Information and Data
6 A Understanding the major functions of a business and how those business functions interact Semester 1 internally and externally so the company can be competitive in a changing market. How information systems can be used and managed in a business. How to critically analyse a business and determine its options for transformation. Desirable Experience as a member of a project team
C INFS5002 or COMP5206 or QBUS5001
INFS6023
Data Visualisation for Managers
6  
INFS6024
Managing Data at Scale
6  
ITLS6111
Spatial Analytics
6 A Basic knowledge of Excel is assumed.
N ITLS6107 or TPTM6180
This unit will use R programming language to perform statistical analyses and spatial analyses. No prior programming knowledge is required.
MKTG6010
Machine Learning in Marketing
6 P BUSS6002 OR QBUS5011
MKTG6018
Customer Analytics and Relationship Management
6  
QBUS6310
Business Operations Analysis
6 P ECMT5001 or QBUS5001 or QBUS5002
N ECMT6008
QBUS6810
Statistical Learning and Data Mining
6 P (ECMT5001 or QBUS5001 or STAT5003) and (a mark of 65 or greater in BUSS6002 or COMP5310 or COMP5318)
Students should complete BUSS6002 before enrolling in this unit as QBUS6810 builds on the material covered in BUSS6002.
QBUS6820
Prescriptive Analytics: From Data to Decision
6 A Vectors, matrices, probability, Python
P ECMT5001 or QBUS5001
C BUSS6002
QBUS6830
Financial Time Series and Forecasting
6 A Basic knowledge of quantitative methods including statistics, basic probability theory, and introductory regression analysis
P ECMT5001 or QBUS5001
QBUS6840
Predictive Analytics
6 P (QBUS5001 or ECMT5001 or STAT5003) and (a mark of 65 or greater in BUSS6002 or COMP5310 or COMP5318)
QBUS6850
Machine Learning for Business
6 P QBUS6810
QBUS6860
Visual Data Analytics
6 A The unit assumes knowledge of statistics and confidence in working with data
P QBUS5001 or QBUS5002
QBUS6952
Behavioral Data Science for Business
6 A The unit assumes knowledge of statistics and confidence in working with data