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
This is the 2020 handbook entry. See the 2027 entry.
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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 participates will be expected to operate as a connected cohort of like-minded people ready to build a better society.
Course structure
Part A. Advanced preparatory24 credit points
Part B. Mastery knowledge48 credit points
- ETC5512Wild-caught dataNo reviews yet6 cp
- ETC5513Collaborative and reproducible practicesNo reviews yet6 cp
- ETC5521Exploratory data analysisNo reviews yet6 cp
- ETC5523Communicating with dataNo reviews yet6 cp
- ETC5543Business analytics creative activityNo reviews yet12 cp
- ETC5580Advanced statistical modellingNo reviews yet6 cp
- ETF5500High dimensional data analysisNo reviews yet6 cp
Part C. Application studies24 credit points
- ETC5410Bayesian time series econometricsNo reviews yet6 cp
- FIT5147Data exploration and visualisationNo reviews yet6 cp
- FIT5205Data in societyNo reviews yet6 cp
- FIT5212Data analysis for semi-structured dataNo reviews yet6 cp
- FIT9132Introduction to databasesNo reviews yet6 cp
- FIT9136Algorithms and programming foundations in PythonNo reviews yet6 cp
- MAT9004Mathematical foundations for data scienceNo reviews yet6 cp
The handbook's description of this structure
The course is structured in three parts: Part A. Advanced preparatory, Part B. Mastery knowledge and Part C. Application studies. All students complete Part B and C. Depending upon prior qualifications, you may receive credit for Part A.
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
- If you are admitted at Entry level 1 you complete 96 points, comprising Part A, B and C
- If you are admitted at Entry level 2 you complete 72 points, comprising Part B and 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.
The course progression map provides guidance on unit enrolment for each semester of study.
The course comprises 96 points structured into three parts: Part A. Advanced preparatory (24 points), Part B. Mastery knowledge (48 points) and Part C. Application studies (24 points)
- If you are admitted at Entry level 1 you complete 96 points, comprising Part A, B and C
- If you are admitted at Entry level 2 you complete 72 points, comprising Part B and 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.
Units are 6 credit points unless otherwise stated.
Part A: Advanced preparatory (24 points)
You must complete:
- ETC5242 Statistical thinking
- ETC5250 Introduction to machine learning
- ETC5510 Introduction to data analysis
- ETC5550 Applied forecasting
Part B: Mastery knowledge (48 points)
You must complete:
- ETC5512 Wild-caught data
- ETC5513 Collaborative and reproducible practices
- ETC5521 Exploratory data analysis
- ETC5523 Communicating with data
- ETC5543 Business analytics creative activity (12 points)
- ETC5580 Advanced statistical modelling
- ETF5500 High dimensional data analysis
Part C: Application studies (24 points)
You must complete four units (24 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 time series econometrics
- 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
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 minimum: Level A, that is: IELTS: 6.5 overall (no band lower than 6.0); or TOEFL Paper-based test: 550 with TWE of 4.5; 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
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 participates will be expected to operate as a connected cohort of like-minded people ready to build a better society.
Contacts
- Academic Coordinator
- Professor Dianne Cook
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.