Master of Data Analytics

COURSE (INTERNATIONAL STUDENTS)

Overview

Deakin’s Master of Data Analytics prepares students for professional employment across all sectors.  The sheer volume and complexity of data already at the fingertips of businesses and research organisations gives rise to challenges that must be solved by tomorrow’s graduates.  Become a data analytics specialist capable of using data to learn insights and support decision making.

Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions.

Throughout your studies you’ll learn to understand the various origins of data to be used for analysis, combined with methods to manage, organise and manipulate data within regulatory, ethical and security constraints. You’ll develop specialised skills in categorising and transferring raw data into meaningful information for the benefit of prediction and robust decision-making.

As a graduate, your knowledge, skills and competencies in modern data science and statistical analysis will be highly valued by employers seeking greater efficiencies and competitive advantage through data insights.

Units in the course may include assessment hurdle requirements.

Read More VIEW DOMESTIC COURSE INFORMATION

Key facts

English language requirements

Overall IELTS score of 6.5 with no band less than 6 (or equivalent). More information is available at www.ielts.org

Duration

1.5-2 years full-time or part-time equivalent depending on your entry point

Campuses

Offered at Burwood (Melbourne)

Cloud (online)

Trimester 1

  • Start date: March
  • Available at:
    • Burwood (Melbourne)
    • Cloud Campus

Trimester 2

  • Start date: July
  • Available at:
    • Burwood (Melbourne)
    • Cloud Campus

Trimester 3

  • Start date: November
  • Available at:
    • Burwood (Melbourne)
    • Cloud Campus

Key information

Award granted

Master of Data Analytics

Year

2017 course information

Estimated tuition fee - full-fee paying place

Deakin code

S777

CRICOS code

089186E

Level

Higher Degree Coursework (Masters and Doctorates)

Approval status

This course is approved by the University under the Higher Education Standards Framework.

Australian Quality Framework (AQF) recognition

The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9.

Entry requirements

Deakin University offers admission to postgraduate courses through a number of Admission categories.

In all categories of admission, selection is based primarily on academic merit as indicated by an applicant's previous academic record. The minimum requirements are successful completion of a three-year undergraduate degree, or equivalent, from an approved university or other educational institution or successful completion of other equivalent qualifications gained by examination, or approved professional or industrial experience.

International students must also meet the postgraduate English language requirements.

Bachelor degree in any discipline.

Career outcomes

Graduates of this course may find careers as data analysts, data scientists, analytics programmers, analytics managers, analytics consultants, business analysts, management advisors, management analysts, business advisors and strategists, marketing managers, market research analysts and marketing specialists.

Course learning outcomes

Deakin Graduate Learning Outcomes (DGLOs)

Course Learning Outcomes (CLOs)

1. Discipline-specific knowledge and capabilities: appropriate to the level of study related to a discipline or profession.

  • Develop a broad, coherent knowledge of the analytics discipline, including: the origin and characteristics of data; the methods and approaches to dealing with data appropriately and securely; and how the use of analytics outcomes can be used to improve business, organisations or society.
  • Apply advanced knowledge and skills to decompose complex processes (from real world situations) to develop data analytics solutions for use in modern organisations across multiple industry sectors.
  • Assess the role data analytics plays in the context of modern organisations and society in order to add value.

2. Communication: using oral, written and interpersonal communication to inform, motivate and effect change.

  • Communicate effectively in order to design, evaluate and respond to advances in data analytics approaches, technology, future trends and industry standards and utilise a range of verbal, graphical and written forms, customised for diverse audiences including specialist and non- specialist clients, colleagues and industry personnel.

3. Digital literacy: using technologies to find, use and disseminate information.

  • Utilise a range of digital technologies and information sources to discover, select, analyse, synthesise, evaluate, critique and disseminate both technical and professional information.

4. Critical thinking: evaluating information using critical and analytical thinking and judgment.

  • Appraise complex information using critical and analytical thinking and judgement to identify problems, analyse user requirements and propose appropriate and innovative solutions.

5. Problem solving: creating solutions to authentic
(real world and
ill-defined) problems.

  • Generate data solutions through the application of specialised theoretical constructs, expert skills and critical analysis to real-world, ill-defined problems to develop appropriate and innovative IT solutions.

6. Self-management: working and learning independently, and taking responsibility for personal actions.

  • Take personal, professional and social responsibility within changing national and international professional IT contexts to develop autonomy as researchers and evaluate own performance for continuing professional development.
  • Work autonomously and responsibly to create solutions to new situations and actively apply knowledge of theoretical constructs and methodologies to make informed decisions.

7. Teamwork: working and learning with others from different disciplines and backgrounds.

  • Work independently and collaboratively towards achieving the outcomes of a group project, thereby demonstrating interpersonal skills including the ability to brainstorm, negotiate, resolve conflicts, manage difficult and awkward conversations, provide constructive feedback, and demonstrate the ability to function effectively in diverse professional, social and cultural contexts.

8. Global citizenship: engaging ethically and productively in the professional context and with diverse communities and cultures in a global context.

  • Engage in professional and ethical behaviour in the design, development and management of IT systems, in the global context, in collaboration with diverse communities and cultures.

 Approved by Faculty Board 14 July 2016

Course Structure

To complete the Master of Data Analytics, students must attain 16 credit points. Most units (think of units as ‘subjects’) are equal to 1 credit point. So that means in order to gain 16 credit points, you’ll need to study 16 units (AKA ‘subjects’) over your entire degree. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.

The 16 credit points include 12 core units (these are compulsory) and 4 course-grouped elective units (comprising Project units, Industry Placement and/or level 7 elective units).  

Students who are entering with a related academic/professional background may be eligible for credit transfer and recognition for up to 4 of the foundation units.  Please contact your course advisor for more detailed information.

 

12

Core units

4

Elective units (Course-grouped)

16

Total units

Core

Year 1 - Trimester 1

  • Foundation Skills in Data Analysis MIS770
  • Value of Information MIS782
  • Database and Information Retrieval SIT772
  • Real World Analytics SIT718
  • Year 1 - Trimester 2

  • Descriptive Analytics and Visualisation MIS771
  • Predictive Analytics MIS772
  • Security and Privacy Issues in Analytics SIT719
  • Machine Learning SIT720

  • Year 2 - Trimester 1

  • Statistical Data Analysis SIT741
  • Modern Data Science SIT742
  • plus two course grouped elective units

    Year 2 - Trimester 2

  • Multivariate and Categorical Data Analysis SIT743
  • Practical Machine Learning for Data Science SIT744
  • plus two course grouped elective units

    Electives

    Four (4) level 7 SIT/MIS course grouped elective units, which may include the following:

  • Major Thesis SIT790 (4cp)
  • Professional Practice SIT791 (4cp)*
  • Minor Thesis SIT792
  • How to apply

    Apply direct to Deakin

    Applications must be made directly to the University through the Applicant Portal. For information on the application process and closing dates, see the Apply web page. Please note that closing dates may vary for individual courses.

    Apply through Deakin

    Deakin International office or Deakin representative

    Fill out the application form and submit to a Deakin International office or take your application form to a Deakin representative for assistance

    PDF Application form - 306 KB

    Need more information on how to apply?

    For information on the application process and closing dates, see the How to apply webpage
    If you’re still having problems, please contact Deakin International for assistance.

    Course pathways

    Credit for Prior Learning

    Am I eligible to receive credit for prior learning?

    The University aims to provide students with as much credit as possible for approved prior study or informal learning which exceeds the normal entrance requirements for the course and is within the constraints of the course regulations. Students are required to complete a minimum of one-third of the course at Deakin University, or four credit points, whichever is the greater. In the case of certificates, including graduate certificates, a minimum of two credit points within the course must be completed at Deakin.

    You can also refer to the Credit for Prior Learning System which outlines the credit that may be granted towards a Deakin University degree.

    Workload

    You can expect to participate in a range of teaching activities each week. This could include classes, seminars, practicals and online interaction. You can refer to the individual unit details in the course structure for more information. You will also need to study and complete assessment tasks in your own time.

    Work experience

    You will have an opportunity to undertake a discipline-specific internship placement as part of your course. deakin.edu.au/sebe/wil.

    Scholarship options

    A Deakin scholarship could help you pay for your course fees, living costs and study materials. If you've got something special to offer Deakin - or maybe you just need a bit of extra support - we've got a scholarship opportunity for you. Search or browse through our scholarships

    Offered campuses

    Burwood

    Just 30 minutes from the city centre, the Melbourne Burwood Campus is Deakin's thriving metropolitan campus.


    Study online at Cloud Campus

    Students are able to study all or part of this course online. You can study anywhere, anytime through Deakin's Cloud Campus.

    Learn more about studying online and the Cloud Campus

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