B6022 Master of Business Analytics
Faculty of Business and Economics
Master of Business Analytics (B6022) is a 2 years full-time, 96-credit-point, master's degree (coursework) course from the Faculty of Business and Economics, taught at Clayton. Map your units semester by semester with the MonMap planner.
- Credit points
- 96
- Duration
- 2 years full time
- 4 years part time
- Campus
- Clayton
- On campus
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Requisite map
Overview
Become empowered as a quantitative citizen. By leveraging open data and the most powerful open source software available today you will learn how to fish, rather than be fed. This course is designed to develop thinking, computing and analytic skills for working with data for evidence-based solutions of today's problems. You will learn how to critically assess information provided by others by sourcing and analysing data yourself, with a strong emphasis on ethical and reproducible methods. This course is suitable for those with undergraduate degrees in quantitative disciplines including mathematics, statistics, computer science and engineering. The content is student-centred, inclusive, accessible and participants will be expected to operate as a connected cohort of like-minded people ready to build a better society.
Course structure
Part A. Advanced preparatory studies24 credit points
Note: You must complete either ETC5550 or ETF5231.
Part B. Core studies48 credit points
- 36 credit points (five units) from the following list, and
- 12 credit points (two units) chosen from the specified discipline electives.
- ETC5512Wild-caught dataNo reviews yet6 cp
- ETC5513Collaborative and reproducible practicesNo reviews yet6 cp
- ETC5521Diving deeply into data explorationNo reviews yet6 cp
- ETC5523Communicating with dataNo reviews yet6 cp
- ETC5543Business analytics creative activityNo reviews yet12 cp
Specified discipline electives
12 credit pointsNote: You are encouraged to take all four units (24 credit points). Any additional units can be taken as substitutes for Part C. Elective studies.
Part C. Elective studies24 credit points
Note: It is recommended that you complete electives from the following list.
- ETC5410Bayesian inference and data analysisNo reviews yet6 cp
- ETF5248Optimisation for businessNo reviews yet6 cp
- ETF5480Decision modelling for businessNo reviews yet6 cp
- FIT5147Data exploration and visualisationNo reviews yet6 cp
- FIT5237Responsible digitalisationNo reviews yet6 cp
- FIT5212Data analysis for semi-structured dataNo reviews yet6 cp
- FIT9132Introduction to databasesNo reviews yet6 cp
- FIT9136Introduction to Python programmingNo reviews yet6 cp
- MAT9004Mathematical foundations for data science and AINo reviews yet6 cp
The handbook's description of this structure
The course is structured in three parts: Part A. Advanced preparatory studies, Part B. Core studies and Part C. Elective studies.
Part A. Advanced preparatory studies
These units will provide you with the knowledge base required for advanced studies in business analytics.
Part B. Core studies
These units will develop your understanding of sourcing and analysing data and will enable you to provide discipline based solutions.
Part C. Elective 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 studies (24 credit points), Part B. Core studies (48 credit points) and Part C. Elective studies (24 credit points)
Units are 6 credit points unless otherwise stated.
Part A: Advanced preparatory studies (24 credit points)
You must complete four units from the following (24 credit points).
Note: You must complete either ETC5550 or ETF5231.
- ETC5242 Statistical thinking
- ETC5250 Introduction to machine learning
- ETC5510 Introduction to data analysis
- ETC5550 Applied forecasting or ETF5231 Business forecasting
Part B: Core studies (48 credit points)
You must complete 48 credit points, comprising:
- 36 credit points (five units) from the following list, and
- 12 credit points (two units) chosen from the specified discipline electives.
- 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
Specified discipline electives (12 credit points)
You must complete at least two of the following units (12 credit points)
Note: You are encouraged to take all four units (24 credit points). Extra units can be taken as substitutes for Part C. Elective studies
- ETC5450 Advanced R programming
- ETC5555 Statistical machine learning
- ETC5580 Advanced statistical modelling
- ETX5500 High dimensional data analysis
Part C: Elective 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.
Note: It is recommended that you 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
- FIT5237 Responsible digitalisation
- FIT5212 Data analysis for semi-structured data
- FIT9132 Introduction to databases
- FIT9136 Introduction to Python programming
- MAT9004 Mathematical foundations for data science and AI
Learning outcomes
These course outcomes are aligned with the Australian Qualifications Framework and Monash Graduate Attributes.
Upon successful completion of this course it is expected that you will be able to:
- 1
critical and creative scholars who:
- produce innovative solutions to data analysis problems
- apply research skills to business challenges
- communicate effectively and perceptively
- 2
responsible and effective global citizens who:
- engage in an internationalised world
- exhibit cross-cultural competence
- demonstrate ethical values
- 3
effective data analysts able to:
- identify and collect appropriate and relevant data
- write computer scripts and programs to wrangle and plot data, fit models, make predictions and produce reproducible reports, presentations and web apps
- interpret statistical models in the context of real data problems, and translate technical results into practical solutions.
Entry requirements
English language
Monash Level A, that is: IELTS (Academic): 6.5 overall (no band lower than 6.0); or Pearson Test of English (Academic): score of 58 overall with no band lower than 50; or TOEFL Internet-based test: score of 79 overall with minimum scores: Writing: 21, Listening: 12, Reading: 13 and Speaking: 18; or Equivalent approved English test
More information
Notes for students
You can enrich your degree by honing your academic and professional skills through the Monash Innovation Guarantee (MIG). The MIG is your unique opportunity to collaborate with renowned industry leaders and design innovative solutions that result in real social change. MIG will give you the leadership skills you will need to adapt and thrive in a rapidly changing world and is your chance to make connections before you graduate. The MIG unit may be credited in place of a free elective as a 6 credit point unit option. For information on eligibility please see FREE.
Other information
Become empowered as a quantitative citizen. By leveraging open data and the most powerful open source software available today you will learn how to fish, rather than be fed. This course is designed to develop thinking, computing and analytic skills for working with data for evidence-based solutions of today's problems. You will learn how to critically assess information provided by others by sourcing and analysing data yourself, with a strong emphasis on ethical and reproducible methods. This course is suitable for those with undergraduate degrees in quantitative disciplines including mathematics, statistics, computer science and engineering. The content is student-centred, inclusive, accessible and participants will be expected to operate as a connected cohort of like-minded people ready to build a better society.
Common questions
How long is Master of Business Analytics?
2 years full time, 96 credit points. At 24 credit points a semester, that is 4 semesters of full-time study.
Where can I study Master of Business Analytics?
At Clayton.
How do I plan my Master of Business Analytics units?
Open the course in the MonMap planner. It lays out your semesters, checks prerequisites as you drag units in, and tracks the credit points each requirement still needs.