GC170 - Graduate Certificate in Analytics

Go to Enrolment Program Structures Search

Plan: GC170 - Graduate Certificate in Analytics
Campus: City Campus

Program delivery and structure

Approach to learning and assessment
Work integrated learning
Program structure

Approach to learning and assessment

The Graduate Certificate in Analytics program is offered through a flexible combination of lectures and tutorials. Some courses will be delivered online or have both online and face-to-face activities. There are opportunities for you to participate in teamwork on projects and be engaged in consulting activities. If you have any special needs during your time as a student, provisions will include extra tutorials and special assistance.

The following are details of learning activities contained in the program:

  • Primarily, you will be learning through lecturers delivering course and other relevant materials. Online materials can be accessed through the RMIT online system -  myRMIT (www.rmit.edu.au/myrmit). The lecturers will elucidate course materials through explanation of key concepts. This will be further illustrated with demonstrations and examples.
  • Assessment will test your understanding of course materials. Provision for this will include written assignments and/or project works and written tests.
  • Tutorials will provide you with extra assistance if you encounter difficulties. Content of the tutorials will also enhance problem-solving skills.
  • Group participation through discussions and seminar presentations will encourage teamwork.
  • Consulting project works will provide practice in the application of theory, through analysis of real data.
  • You are encouraged to seek learning materials from other sources such as libraries and the internet.
  • State-of-the-art statistical and operations research software used in the program will provide you with hands-on-experience required for a statistical analysis of data.

If you have a long-term medical condition, disability and/or other form of disadvantage, it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program manager or the Equitable Learning Service if you would like to find out more.

If you have already developed areas of skill and knowledge included in this program (for example, through prior studies or work experience), you can apply for credit once you have enrolled in this program. There is information on the RMIT University website about how to apply for Recognition of Prior Learning (RPL) – refer to: https://www.rmit.edu.au/students/student-essentials/enrolment/apply-for-credit

 

*Top of page

Work integrated learning

RMIT University is committed to providing you with an education that strongly links formal learning with professional or vocational practice. As a student enrolled in this RMIT University program you will:

  • undertake and be assessed on structured activities that allow you to learn, apply and demonstrate your professional or vocational practice
  • interact with industry and community when undertaking these activities
  • complete these activities in real work contexts or situations.

These interactions and the work context provide a distinctive source of feedback to you to assist your learning.

Any or all of these aspects of a WIL experience may be in a simulated workplace environment.


Work Integrated Learning (WIL) courses include:
In MATH2349 Data Wrangling you will develop and apply your data preprocessing skills to complex, noisy, and inconsistent real-world data using leading open-source software.

 

*Top of page

Program Structure

To graduate you must complete the following: All courses may not be available each semester.
 

*Top of page


Year One of Program
Complete the following Three (3) Courses;

Course Title

Credit Points

Course Code

Campus
Applied Analytics 12 MATH1324 City Campus
Data Wrangling 12 MATH2349 City Campus
Database Concepts 12 ISYS1055 City Campus
AND
Select and complete One (1) of the following Courses;

Course Title

Credit Points

Course Code

Campus
Essential Mathematics 12 MATH2267 City Campus
Machine Learning 12 MATH2319 City Campus
Time Series Analysis 12 MATH1318 City Campus
Data Visualisation and Communication 12 MATH2270 City Campus
Introduction to Statistical Computing 12 MATH1322 City Campus
Introduction to Information Security 12 INTE1120 City Campus
 

*Top of page


 
 
[Previous: Learning outcomes]