Graduate Diploma of Data Analytics

COURSE (INTERNATIONAL STUDENTS)

Overview

The Graduate Diploma of Data Analytics provides you with the specialist knowledge required to formulate solutions to complex data problems across a range of business settings – turning raw data sets into meaningful information to better guide business decisions.

This course aims to prepare you for specialised careers in data analytics by providing you with advanced working knowledge in statistics and data science. You’ll draw on previous studies in IT, computing or related disciplines to focus on the specific application of data analysis in a business setting.

You’ll get an understanding of the various origins of data to be used for analysis, combined with the central approaches and methods to manage, organise and manipulate such data, within regulatory, ethical and security constraints. You’ll learn fundamental aspects of data science, modern methods, techniques and applications of data science.

You’ll learn how to identify problems and understand the various techniques and methods that can be used to solve them. You’ll be able to extract meaning from, make sense of, and draw conclusions from data. These skills will enable you to approach and represent different types of data, and to perform data analysis and statistical inference tasks of interest.

Data security and its governance have now become key issues for all organisations. As such, the various ethical, regulatory and governance aspects of analytics systems will be covered. Potential privacy and security issues associated with large data sets and the results obtained from analytics on such data are examined in detail.

As a graduate, you’ll be ready for a career as a software developer, computer systems analyst/architect/engineer, marketing manager, market research analyst and marketing specialist, management analyst, web developer, network and computer systems administrator, IT project manager, or computer or IT research scientist.

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 year full-time or part-time equivalent

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

*Trimester 3 intake is only offered on a part-time basis.

Key information

Award granted

Graduate Diploma of Data Analytics

Year

2018 course information

Estimated tuition fee - full-fee paying place

Deakin code

S677

CRICOS code

089187D

Level

Postgraduate (Graduate Certificate and Graduate Diploma)

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 8.

Course Structure

To complete the Graduate Diploma of Data Analytics, students must attain 8 credit points. Most units (think of units as ‘subjects’) are equal to 1 credit point. So that means in order to gain 8 credit points, you’ll need to study 8 units (AKA ‘subjects’) over your entire degree. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.

The 8 credit points include 6 core units (these are compulsory) and 2 course grouped elective units (you can choose which ones to study from a specified list).

6

Core units

2

Elective units

8

Total units

Core

Year 1 - Trimester 1

  • Academic Integrity STP050 (0 credit points)
  • Statistical Data Analysis SIT741
  • Modern Data Science SIT742
  • Plus two (2) course grouped elective units

    Year 1 - Trimester 2

  • Security and Privacy Issues in Analytics SIT719
  • Machine Learning SIT720
  • Multivariate and Categorical Data Analysis SIT743
  • Practical Machine Learning for Data Science SIT744
  • Electives

    Select the remaining 2 credit points from a range of level 7 SIT/MIS course grouped elective units.

    Entry requirements

    General admission requirements for entry into postgraduate courses for international students at Deakin are summarised in the undergraduate admission requirements table.
    Some courses may have additional entry requirements

    Students must also meet the undergraduate English language requirements.

    • Bachelor degree in a related discipline.

    IELTS / English language requirements

    Please note that English language requirements exist for entry to this course and you will be required to meet the English language level requirement that is applicable in the year of your commencement of studies.

    It is the students’ responsibility to ensure that she/he has the required IELTS score to register with any external accredited courses.  (more details)

    For more information on the Admission Criteria and Selection (Higher Education Courses) Policy visit the Deakin Policy Library.

    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

    Course Learning Outcomes

    Discipline-specific knowledge and capabilities

    Develop specialised knowledge of data analytics concepts and technologies to solutions based on specifications and user requirements. 

    Communication

    Communicate data analytical solutions as appropriate to the context to inform, motivate and effect change utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences. 

    Digital literacy

    Use digital media to locate, collect and evaluate information from technical channels and apply information to design approaches and solutions that meet user requirements.

    Critical thinking

    Use the frameworks of logical and analytical thinking to evaluate data analytics information, technical problems and user requirements, and develop approaches to identify solutions. 

    Problem solving

    Design solutions for automating data analysis processes by investigating technical and business problems; design and propose alternative solutions that improve services and user experiences.

    Self-management

    Demonstrate the ability to work in a professional manner, learn autonomously and responsibly in order to identify and meet development needs.

    Global citizenship

    Engage in professional and ethical behaviour in the design of data analytics systems, in a global context, in collaboration with diverse communities and cultures.

    Approved by Faculty Board 27 July 2017

    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

    Further study options

    S777 Master of Data Analytics

    Credit for Prior Learning

    Am I eligible to receive credit for prior learning?

    If you have completed previous studies which you believe may reduce the number of units you have to complete at Deakin, indicate in the appropriate section on your application that you wish to be considered for credit for prior learning. You will need to provide a certified copy of your previous course details so your credit can be determined. If you are eligible, your offer letter will then contain information about your credit for prior learning.
    Your credit for prior learning is formally approved prior to your enrolment at Deakin during the Enrolment and Orientation Program. You must bring original documents relating to your previous study so that this approval can occur.

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

    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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