Data Analytics for Business
Unit of study table (Table A)
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. |
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition |
|---|---|---|
Table 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 |
Table A - Data Analytics for Business | ||
Core units of study | ||
| BUSS6002 Data Science in Business | 6 | A Basic mathematical knowledge, e.g., probability, linear algebra, and calculus. C QBUS5001 or QBUS5002 |
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 Data Analytics 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 Machine Learning for Business | 6 | P (ECMT5001 or QBUS5001) and (a mark of 65 or greater in BUSS6002 or COMP5310) |
| 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 Advanced 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 |