B6022 Master of Business Analytics

The course is structured in three parts: Part A. Advanced preparatory, Part B. Mastery knowledge and Part C. Application studies.

Part A. Advanced preparatory

These units will provide you with the knowledge base required for advanced studies in business analytics.

Part B. Mastery knowledge

These units will develop your understanding of sourcing and analysing data and will enable you to provide discipline based solutions.

Part C. Application studies

The focus of these studies is professional or scholarly work that can contribute to a portfolio of professional development. This will be achieved by selecting complementary units offered across the university.

Master's entry points

Depending on prior qualifications you may receive entry level credit (a form of block credit) which determines your point of entry to the course:

• If you are admitted at Entry level 1 you complete 96 credit points, comprising Part A, Part B and Part C
• If you are admitted at Entry level 2 you complete 72 credit points, comprising Part B and Part C

Note: Students eligible for credit for prior studies may elect not to receive the credit and complete one of the higher credit point options.

Course progression map

The course progression map provides guidance on unit enrolment for each semester of study.

The course comprises 96 credit points structured into three parts: Part A. Advanced preparatory (24 credit points), Part B. Mastery knowledge (48 credit points) and Part C. Application studies (24 credit points)

Units are 6 credit points unless otherwise stated.

Part A: Advanced preparatory (24 credit points)

You must complete the following four units (24 credit points):

• ETC5242 Statistical thinking
• ETC5250 Introduction to machine learning
• ETC5510 Introduction to data analysis
• ETC5550 Applied forecasting

Part B: Mastery knowledge (48 credit points)

You must complete the following five units (36 credit points) :

• ETC5512 Wild-caught data
• ETC5513 Collaborative and reproducible practices
• ETC5521 Diving deeply into data exploration
• ETC5523 Communicating with data
• ETC5543 Business analytics creative activity (12 credit points)

and at least two of the following units (12 credit points)*:

• ETC5450 Advanced R programming
• ETC5555 Statistical machine learning
• ETC5580 Advanced statistical modelling
• ETF5500 High dimensional data analysis

*You are encouraged to take all four units (24 credit points). Extra units can be taken as substitutes for Part C. Application studies.

Part C: Application studies (24 credit points)

You must complete four units (24 credit points) at level 5 from the Faculty of Business and Economics or across the University providing you have met the prerequisites and there are no restrictions on enrolling in the units.

You are recommended to complete electives from the following list:

• ETC5410 Bayesian inference and data analysis
• ETF5248 Optimisation for business
• ETF5480 Decision modelling for business
• FIT5147 Data exploration and visualisation
• FIT5205 Data in society
• FIT5212 Data analysis for semi-structured data
• FIT9132 Introduction to databases
• FIT9136 Algorithms and programming foundations in Python
• MAT9004 Mathematical foundations for data science