Table A - Data Analytics for Business
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 specialsiation only. For details of the Table A - Core, Table - 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.
Errata
| Item | Errata | Date |
|---|---|---|
| 1. |
The following unit was omitted from the table. It is available as a selective unit for the Data Analytics in Business specialisation. It will be available in Semester 1. MKTG6010 Machine Learning in Marketing |
25/1/2021 |
| 2. |
The following unit has been cancelled for Semester 1, 2021. It will continue to be offered for Semester 2. MKTG6018 Customer Analytics and Relationship Management |
25/1/2021 |
| 3. |
The following unit was omitted from the table. It is available as a selective unit for the Data Analytics in Business specialisation. It will be available in Semester 1. MKTG6999 Customer Social Data Analytical ToolsCredit points: 6 Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: BUSS6002 Prohibitions: MKTG6998 Assumed knowledge: Python programming, as covered in BUSS6002. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units |
25/1/2021 |
| 4. |
Prerequisites and Corequisites have changed for the following unit. They now read: QBUS6810 Statistical Learning and Data Mining |
1/2/2021 |
| 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 Refer to the unit of study outline https://www.sydney.edu.au/units |
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 Refer to the unit of study outline https://www.sydney.edu.au/units |
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 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. (ii) Desirable Experience as a member of a project team. C INFS5002 or COMP5206 or QBUS5001 Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 |
| INFS6023 Data Visualisation For Managers |
6 |
Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 |
| INFS6024 Managing Data at Scale |
6 | Semester 2 |
|
| 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 |
Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 Semester 2 |
| MKTG6999 Customer Social Data Analytical Tools |
6 | A Python programming, as covered in BUSS6002. P BUSS6002 N MKTG6998 Note. This unit uses Python programming. The related unit of study MKTG6998 will examine social media data from angles without the need for Python programming. |
Semester 2 |
| QBUS6310 Business Operations Analysis |
6 | P ECMT5001 or QBUS5001 or QBUS5002 N ECMT6008 Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 2 |
| QBUS6810 Statistical Learning and Data Mining |
6 | P ECMT5001 or QBUS5001 or BUSS6002 Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 Semester 2 |
| QBUS6820 Business Risk Management |
6 | A Knowledge of basic probability theory and familiarity with spreadsheet modelling P ECMT5001 or QBUS5001 Refer to the unit of study outline https://www.sydney.edu.au/units |
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 Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 Semester 2 |
| QBUS6840 Predictive Analytics |
6 | P (QBUS5001 or ECMT5001) and BUSS6002 Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 Semester 2 |
| QBUS6850 Machine Learning for Business |
6 | P QBUS6810 Refer to the unit of study outline https://www.sydney.edu.au/units |
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 Refer to the unit of study outline https://www.sydney.edu.au/units |
Semester 1 Semester 2 |