Table A - Data Analytics for Business
Please check the current students website (Find a unit of study) for up-to-date information on units of study including availability.
The units of study listed in the following table are those available for the current year. Students may also include any units of study, which are additional to those currently listed, which appear under these subject areas in the Business School handbook/website in subsequent years (subject to any prerequisite or prohibition rules).
Please note. The following table lists Table A units for the specialisation only. For details of the Table A - Core, Table A - Foundational, Table A - Capstone, Table A - Selective and Table A - Elective units of study, please refer to Table A for the Graduate Certificate, Graduate Diploma and Master of Commerce or Table A for the Master of Commerce (Extension) sections in this handbook.
Unit outlines will be available through Find a unit outline two weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition | Session |
|---|---|---|---|
Data Analytics for Business |
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| Achievement of a specialisation in Data Analytics for Business requires a minimum of 30 credit points from this table comprising: | |||
| (i) 6 credit points of Table A - Foundational units of study* | |||
| (ii) 6 credit points of Table A - Data Analytics for Business core units of study; and | |||
| (iii) 18 credit points of Table A - 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 section in Table A for the Graduate Certificate, Graduate Diploma and Master of Commerce OR Table A for the Master of Commerce (Extension). | |||
| Students completing this specialisation as an optional second specialisation for the Master of Commerce (Extension) do not need to complete a capstone unit. | |||
Units of study |
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| The units of study are listed below. | |||
Table A - Foundational unit of study* |
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| * 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 |
Intensive February Semester 1 Semester 2 |
Table A - Data Analytics for Business |
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Core units of study |
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| BUSS6002 Data Science in Business |
6 | A Basic knowledge of probability and statistics C QBUS5001 or QBUS5002 |
Semester 1 Semester 2 |
Selective units of study |
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| 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 |
Semester 1 |
| INFS6023 Data Visualisation For Managers |
6 | Semester 2 |
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| INFS6024 Managing Data at Scale |
6 | Semester 1 |
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| 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. |
Semester 2 |
| MKTG6010 Machine Learning in Marketing |
6 | P BUSS6002 |
Semester 1 |
| MKTG6018 Customer Analytics and Relationship Management |
6 | Semester 2 |
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| QBUS6310 Business Operations Analysis |
6 | P ECMT5001 or QBUS5001 or QBUS5002 N ECMT6008 |
Semester 2 |
| QBUS6810 Statistical Learning and Data Mining |
6 | P (ECMT5001 or QBUS5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318) Students should complete BUSS6002 before enrolling in this unit as QBUS6810 builds on the material covered in BUSS6002. |
Semester 1 Semester 2 |
| QBUS6820 Business Risk Management |
6 | A Knowledge of basic probability theory and familiarity with spreadsheet modelling P ECMT5001 or QBUS5001 |
Semester 2 |
| 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 |
Semester 1 Semester 2 |
| QBUS6840 Predictive Analytics |
6 | P (QBUS5001 or ECMT5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318) |
Semester 1 Semester 2 |
| QBUS6850 Machine Learning for Business |
6 | P QBUS6810 |
Semester 1 Semester 2 |
| QBUS6860 Visual Data Analytics |
6 | A The unit assumes knowledge of statistics and confidence in working with data P QBUS5001 or QBUS5002 |
Semester 1 Semester 2 |
| QBUS6952 Behavioral Data Science for Business |
6 | A The unit assumes knowledge of statistics and confidence in working with data |
Semester 2 |