Data Analytics

Unit of study table

Master of Data Analytics

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

Core

ODAT5011
Data Analytics Foundations
6 A Year 10 mathematics or equivalent
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 A good understanding of statistics, including hypothesis testing and regression modelling, and substantial statistical computing experience. For example, both ODAT5011 and ODAT5021 or a unit like STAT5002.
Selective Units
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 Effects in Practice
6 A Strong statistical knowledge and coding experience (e.g., successful completion of ODAT5011, ODAT5021 and ODAT5022)
P
ODAT5021
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). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent)
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
ODAT5888
Data Sleuthing
6 This unit of study is being developed, subject to change

Graduate Diploma of Data Analytics

Core
ODAT5011
Data Analytics Foundations
6 A Year 10 mathematics or equivalent
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 A good understanding of statistics, including hypothesis testing and regression modelling, and substantial statistical computing experience. For example, both ODAT5011 and ODAT5021 or a unit like STAT5002.
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 This unit of study is being developed, subject to change
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 Effects in Practice
6 A Strong statistical knowledge and coding experience (e.g., successful completion of ODAT5011, ODAT5021 and ODAT5022)
P ODAT5021
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). Discrete mathematics and probability (e.g. MATH1064 or equivalent); linear algebra and calculus (e.g. MATH1061 or equivalent)
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

Core Units
ODAT5011
Data Analytics Foundations
6 A Year 10 mathematics or equivalent
ODAT5012
Data Visualisation and Communication
6  
ODAT5013
Data Wrangling and Databases
6 A Basic computer literacy
ODAT5014
Data Governance and Ethics
6