About this course
Phone: + 61 3 8344 0149
Contact hours: https://unimelb.edu.au/professional-development/contact-us
|Award title||Graduate Certificate in Applied Analytics|
|Year & campus||2020 — Parkville|
|Fees information||Subject EFTSL, level, discipline and census date|
|Study level & type||Graduate Coursework|
|Credit points||50 credit points|
|Duration||12 months part-time|
The Graduate Certificate in Applied Analytics targets professionals seeking to develop an understanding of how to apply analytics in their profession. The course will enable students to develop a sound understanding of analytics technology and equip them to apply key analytics techniques to different scenarios in their professional area and evaluate and communicate strategic and operational issues around the application of analytics concepts and theories to real-world practice.
The degree requires completion of two compulsory subjects and two elective subjects. Students who complete the Graduate Certificate have the option of proceeding to the Master of Applied Analytics which will involve completion of a further two subjects plus a Capstone project. Credit will be granted towards this Master’s degree for the four subjects completed in the Graduate Certificate.
All subjects are undertaken online providing students with flexibility in where and when they study. Students will be expected to actively participate in discussions and collaborations with their peers and teachers.
As most students in this course will be full-time professionals, enrolment is currently offered on a part-time basis only, requiring students to complete one subject per eight-week term, over an academic year of four terms. The course will typically be completed over one year part time.
1. In order to be considered for entry, applicants must have completed:
- a bachelor honours degree or equivalent in a cognate area; or
- a three-year undergraduate qualification and at least 50 credit points, or equivalent, of graduate study in a cognate area; or
- a three-year undergraduate qualification in a cognate area and at least two years of documented, relevant work experience; or
- a minimum of eight years documented, relevant work experience; and
- a personal statement.
Meeting these requirements does not guarantee selection.
2. In ranking applications, the Selection Committee will consider:
- prior academic performance; and if relevant
- the work experience; and
- the personal statement.
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 6.5 is required.
(a) Cognate area refers to one of i) Health, ii) Psychology, iii) Urban design/architecture/built environment iv) Education
Inherent requirements (core participation requirements)
The Graduate Certificate of Applied Analytics welcomes applications from students with disabilities. It is University and degree policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student’s participation in the degree.
The Graduate Certificate of Applied Analytics requires all students to enrol in subjects where they will
- The ability to use a computer, including read material on screen, to a competent standard;
- The ability to read, analyse and comprehend complex scenarios involving the use of analytics;
- The ability to actively and safely contribute in online activities;
- The ability to work independently and as a part of a group;
- The ability to clearly and independently communicate orally a knowledge and application of
analytics principles and critically evaluate these principles;
- The ability to clearly and independently communicate in writing a knowledge and application
of analytics principles
Students must possess behavioural and social attributes that enable them to participate in a complex learning environment. Students are required to take responsibility for their own participation and learning. They also contribute to the learning of other students in collaborative learning environments, demonstrating interpersonal skills and an understanding of the needs of other students. Assessment may include the outcomes of tasks completed in collaboration with other students. There may be additional inherent academic requirements for some subjects, and these requirements are listed within the description of the requirements for each of these subjects.
Students who feel their disability will impact on meeting this requirement are encouraged to discuss this matter with the relevant Subject Coordinator and the Disability Liaison Unit: http://www.services.unimelb.edu.au/disability/
Intended learning outcomes
Upon completion of the course, students should be able to:
- select suitable analytics techniques for a variety of real-world scenarios and demonstrate a specialised understanding of their contextual strengths and limitations, to make a contribution to evidence-based practice
- synthesize and evaluate the factors involved in applying analytics solutions to real-world operational and strategic issues, including different interpretations of unstructured evidence
- create a design for the various components of an overall real-world analytics solution for completion by different members of an analytics project team.
- critique and demonstrate a specialised understanding of the issues involved in real-world operational or strategic applications of analytics and suggest enhancements that improve both the relevance of data that is collected, as well as compliance with current/future privacy requirements
- identify potential ethical issues in the application of an analytics approach, or solution, to a real-world scenario and analyse remedies that comply with the relevant professional code/s of conduct.
Students will be provided with the opportunity to practice and reinforce:
- high level written communication skills
- data manipulation and interpretation skills
- analytic, integration and problem-solving skills
- demonstration of competence in critical and conceptual thinking through report writing and online discussions
- Academic Distinction Graduates will have begun to develop evidence- and practice-based knowledge of analytics that can be applied across disciplines. They will demonstrate a foundational level of skill designing and conducting analytics activities, and communicating with clients and stakeholders. Graduates will have begun to develop as critical and creative thinkers, with an aptitude for continued self-directed learning.
- Active Citizenship Graduates will be prepared to contribute meaningfully to analytics thinking and practice in their professional and community contexts. They will understand the existence of multiple sets of values, how those affect the use of analytics, and how to make the values conversation an explicit component of the application of analytics.
- Integrity and Self-Awareness Graduates will be aware of existing professional standards and competencies for analytics practitioners. They will understand the importance of critically reflecting on their analytics applications and have set the foundation for a strong sense of intellectual integrity regarding analytics.
Two compulsory subjects and two electives.
Students must complete MAST90130 Critical Thinking with Analytics as first subject.
Student must complete MGMT90248 Analytics and Society.
|Code||Name||Study period||Credit Points|
|MAST90130||Critical Thinking with Analytics||
|MGMT90248||Analytics and Society||
25 points (two of the following):
|Code||Name||Study period||Credit Points|
|ABPL90407||Representing Spatial Information||
|MGMT90239||Business Analytics for Decision Making||
|PSYC90109||Introduction to Experience Sampling||
|MAST90135||Foundations of Analytics||
|POPH90295||Designing Analytics Investigations||
|MAST90134||Advanced Elements of Analytics||Not available in 2020||12.5|
Last updated: 1 May 2020