Data Analytics
Unit of study table
| This page was first published on 15 November 2023 and was last amended on 20 May 2024. View details of the changes below. |
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Master of Data Analytics |
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| To qualify for the award of the Master of Data Analytics, a candidate must complete 72 credit points, comprising: |
| (i) 42 credit points of core units |
| (ii) 24 credit points of selective units |
| (iii) 6 credit points of capstone units |
Graduate Diploma in Data Analytics |
| To qualify for the award of the Graduate Diploma in Data Analytics, a candidate must complete 48 credit points, comprising: |
| (i) a minimum of 36 credit points of core units |
| (ii) a maximum of 12 credit points of selective units |
Graduate Certificate in Data Analytics |
| To qualify for the award of the Graduate Certificate in Data Analytics, a candidate must complete 24 credit points, comprising: |
| (i) 24 credit points of core units |
| Unit of study | Credit points | A: Assumed knowledge P: Prerequisites C: Corequisites N: Prohibition |
|---|---|---|
Master of Data Analytics |
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Core |
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| ODAT5011 Data Analytics Foundations |
6 | |
| ODAT5012 Data Visualisation and Communication |
6 | |
| ODAT5013 Data Wrangling and Databases |
6 | A Basic computer literacy |
| ODAT5014 Data Governance and Ethics |
6 | |
| ODAT5021 Study Design and Analysis |
6 | A Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analytics Foundations |
| ODAT5022 Applied time series analysis |
6 | A Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analysis Foundations. It would be an advantage to also take ODAT5021 to further build your statistical and computational skills before attempting ODAT5022. |
| OSTA5003 Computational Statistical Methods |
6 | A STAT5002 or equivalent introductory statistics course with a statistical computing component |
Selective Units |
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| ODAT5031 Modelling Complex Data Structures |
6 | A Strong statistical knowledge and coding experience. E.g. ODAT5011, ODAT5021, and OSTA5003 |
| ODAT5032 Data to Decisions |
6 | |
| OCMP5048 Visual Analytics |
6 | A Experience with data structures and algorithms as covered in COMP9123 or COMP9103 or COMP9003 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP5048 or COMP4448 |
| OECO5017 Causal Inference |
6 | |
| OCMP5318 Machine Learning and Data Mining |
6 | A Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent unit of study from different institutions) N COMP5318 or COMP4318 |
| OCMP5338 Advanced Data Models |
6 | A This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1) N COMP5338 or COMP4338 |
| OCMP5328 Advanced Machine Learning |
6 | C OCMP5318 or COMP5318 or COMP4318 or COMP3308 or COMP3608 N COMP5328 or COMP4328 |
| OCMP5329 Deep Learning |
6 | A OCMP5318 or COMP5318 or COMP4318 N COMP5329 or COMP4329 |
Capstone |
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| ODAT5888 Data Sleuthing |
6 | |
Graduate Diploma of Data Analytics |
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Core |
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| ODAT5011 Data Analytics Foundations |
6 | |
| ODAT5012 Data Visualisation and Communication |
6 | |
| ODAT5013 Data Wrangling and Databases |
6 | A Basic computer literacy |
| ODAT5014 Data Governance and Ethics |
6 | |
| ODAT5021 Study Design and Analysis |
6 | A Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analytics Foundations |
| ODAT5022 Applied time series analysis |
6 | A Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analysis Foundations. It would be an advantage to also take ODAT5021 to further build your statistical and computational skills before attempting ODAT5022. |
Selective |
||
| OSTA5003 Computational Statistical Methods |
6 | A STAT5002 or equivalent introductory statistics course with a statistical computing component |
| ODAT5031 Modelling Complex Data Structures |
6 | A Strong statistical knowledge and coding experience. E.g. ODAT5011, ODAT5021, and OSTA5003 |
| ODAT5032 Data to Decisions |
6 | |
| ODAT5888 Data Sleuthing |
6 | |
| OCMP5048 Visual Analytics |
6 | A Experience with data structures and algorithms as covered in COMP9123 or COMP9103 or COMP9003 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) N COMP5048 or COMP4448 |
| OECO5017 Causal Inference |
6 | |
| OCMP5318 Machine Learning and Data Mining |
6 | A Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent unit of study from different institutions) N COMP5318 or COMP4318 |
| OCMP5338 Advanced Data Models |
6 | A This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1) N COMP5338 or COMP4338 |
| OCMP5328 Advanced Machine Learning |
6 | C OCMP5318 or COMP5318 or COMP4318 or COMP3308 or COMP3608 N COMP5328 or COMP4328 |
| OCMP5329 Deep Learning |
6 | A OCMP5318 or COMP5318 or COMP4318 N COMP5329 or COMP4329 |
Graduate Certificate of Data Analytics |
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Core Units |
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| ODAT5011 Data Analytics Foundations |
6 | |
| ODAT5012 Data Visualisation and Communication |
6 | |
| ODAT5013 Data Wrangling and Databases |
6 | A Basic computer literacy |
| ODAT5014 Data Governance and Ethics |
6 | |
