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Graduate Diploma in Business Analytics (GD-BUSANA)
Graduate DiplomaYear: 2023 Delivered: On Campus (Parkville)
About this course
Director
Simon Holcombe
Overview
Award title | Graduate Diploma in Business Analytics |
---|---|
Year & campus | 2023 — Parkville |
Fees information | Subject EFTSL, level, discipline and census date |
Study level & type | Graduate Coursework |
AQF level | 8 |
Credit points | 100 credit points |
Duration | 24 months part-time |
The goal of this course is to equip its graduates with specialised data analytic techniques. It includes eight core subjects that cover foundation and advanced data analytics in various specialised disciplinary area, as well as frameworks for applying those techniques to its business contexts.
Entry requirements
1. In order to be considered for entry, applicants must have:
- Completed an undergraduate degree of 3 or 4 years minimum in one of the relevant areas listed below, taken at a third year university level from a recognised institution, with a minimum weighted average mark (WAM) of 65% or equivalent.
- Commerce
- Mathematics and/or Physics
- Computer Science or Information Systems
- Engineering and/or Science
- Two years of professional working experiences
Meeting these requirements does not guarantee selection.
2. In ranking applications, the Selection Committee will consider:
- Prior academic performance; and
- Professional working experiences; and
3. The Selection Committee may seek further information to clarify any aspect of an application in accordance with the Academic Board Rules on the use of selection instruments.
4. Applicants are required to satisfy the university’s English language requirements for graduate courses. For those applicants seeking to meet these requirements by one of the standard tests approved by the Academic Board, performance band 7.0 is required. International students must demonstrate proficiency in English either through IELTS score (minimum score requirement of 7.0 overall with no individual score less than 7) or TOEFL IBT score (minimum of 94, and a written score minimum of 27 and no band less than 24).
Note:
GMAT scores may be required if a University of Melbourne credit average equivalence is difficult to obtain.
Inherent requirements (core participation requirements)
Inherent requirements are the abilities, knowledge and skills needed to complete this course that must be met by all students. For information on the inherent requirements specific to this course contact the course/program coordinator. In some circumstances reasonable adjustments may be available to enable students to meet these requirements while still preserving the academic integrity of the university's learning, assessment and accreditation processes. For more information on how to seek these adjustments refer to the Student Equity and Disability Support website: https://services.unimelb.edu.au/student-equity/home
Intended learning outcomes
On completion of this course, graduates should be able to:
- Identify appropriate analytic techniques to address business problems - Students will translate business decision problems into quantitative form. - Students will diagnose a situation and identify appropriate applications of statistical models, data mining and optimisation techniques
- Apply analytical skills and technical methods to develop innovative solutions in business contexts - Students will investigate data structures relevant to business environments and problems - Students will appropriately apply and synthesise key analytics models and techniques to resolve business problems
- Integrate the knowledge and skills acquired to conduct research in an industry setting - Students will appropriately select one or more advanced analytical techniques to apply to a business setting with real data
- Deal with ambiguity and uncertainty - Students will make appropriate judgements about how to proceed in an environment with incomplete information - Students will identify and be able to deal with ambiguities and bias in data.
Generic skills
- Communication and presentation skills
- Negotiation skills
- Influencing skills
- Working in teams
Graduate attributes
- Discipline knowledge and skills
- Business analytical skills
- Teamwork and communication skills
- Creative and critical thinking
- Problem solving skills
Course structure
Core subjects:
Code | Name | Study period | Credit Points |
---|---|---|---|
BUSA90536 | Statistical Learning for Business |
March (On Campus - Parkville)
July (On Campus - Parkville)
|
12.5 |
BUSA90540 | Causal Analytics for Business | May (On Campus - Parkville) |
12.5 |
Selective Core subjects Group 1:
50 credit points from the following subjects:
Code | Name | Study period | Credit Points |
---|---|---|---|
BUSA90539 | Business Data Platforms |
March (On Campus - Parkville)
September (On Campus - Parkville)
|
12.5 |
BUSA90537 | Coding for Business Problems |
March (On Campus - Parkville)
July (On Campus - Parkville)
|
12.5 |
BUSA90538 | Decision Making and Optimisation |
March (On Campus - Parkville)
July (On Campus - Parkville)
|
12.5 |
BUSA90542 | Machine Learning & AI for Business | May (On Campus - Parkville) |
12.5 |
BUSA90541 | Predictive Business Analytics |
May (On Campus - Parkville)
September (On Campus - Parkville)
|
12.5 |
BUSA90543 | Text Analytics for Business |
May (On Campus - Parkville)
September (On Campus - Parkville)
|
12.5 |
Selective Core subjects Group 2:
25 credit points from the following subjects:
Code | Name | Study period | Credit Points |
---|---|---|---|
BUSA90546 | Risk Analytics | October (On Campus - Parkville) |
12.5 |
BUSA90544 | Marketing Analytics | October (On Campus - Parkville) |
12.5 |
BUSA90545 | Supply Chain Analytics |
September (On Campus - Parkville)
October (On Campus - Parkville)
|
12.5 |
(Note: BUSA90545 Supply Chain Analytics's prerequisite subject is BUSA90538 Decision Making and Optimisation.)
Last updated: 30 January 2024