GD120 - Graduate Diploma in Statistics and Operations Research

Go to Enrolment Program Structures Search

Plan: GD120P12 - Graduate Diploma in Statistics and Operations Research
Campus: City Campus

Program delivery and structure

Approach to learning and assessment
Work integrated learning
Program structure
Program transition plan

Approach to learning and assessment

The Graduate Diploma in Statistics and Operations Research program is offered through a flexible combination of lectures, tutorials and computer laboratory classes. Classes are usually held once a week in the evening over a two-hour period. There are also 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 in computer laboratory.

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

  • Primarily, you will be learning face-to-face with the lecturers delivering course and other relevant materials. Online materials can be accessed through an online system called 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 examinations.
  • 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 coordinator 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: www.rmit.edu.au/students/enrolment/credit/he

*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

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

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

In this program, you will be doing specific course(s) that focus on work integrated learning (WIL). You will be assessed on professional or vocational work in a workplace setting (real or simulated) and receive feedback from those involved in your industry.

You will develop your practical skills in Data Wrangling (MATH2349) which includes a Work Integrated Learning (WIL) experience in which your knowledge and skills are applied and assessed in a simulated workplace context.

*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 Four (4) Courses:

Course Title

Credit Points

Course Code

Campus

Mathematical Modelling and Decision Analysis 12 MATH1293 City Campus
Data Wrangling 12 MATH2349 City Campus
Applied Analytics 12 MATH1324 City Campus
Database Concepts 12 ISYS1055 City Campus
AND
Select and Complete Two (2) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Essential Mathematics 12 MATH2267 City Campus
Data Visualisation and Communication 12 MATH2270 City Campus
Applied Bayesian Statistics 12 MATH2269 City Campus
Analysis of Categorical Data 12 MATH1298 City Campus
Design and Analysis of Experiments 12 MATH1302 City Campus
Forecasting 12 MATH1307 City Campus
Multivariate Analysis Techniques 12 MATH1309 City Campus
Regression Analysis 12 MATH1312 City Campus
Statistical Inference 12 MATH1315 City Campus
Statistics of Quality Control and Performance Analysis 12 MATH1316 City Campus
Stochastic Processes and Applications 12 MATH1317 City Campus
Time Series Analysis 12 MATH1318 City Campus
Game Theory and its Applications 12 MATH1320 City Campus
Methods and Models of Operations Research 12 MATH1326 City Campus
Questionnaire and Research Design 12 MATH2218 City Campus
Systems Simulation 12 MATH2219 City Campus
System Dynamics 12 MATH2220 City Campus
Sports Analytics 12 MATH2223 City Campus
Machine Learning 12 MATH2319 City Campus
Introduction to Statistical Computing 12 MATH1322 City Campus
AND
Select and Complete Two (2) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Scripting Language Programming 12 COSC1092 City Campus
Artificial Intelligence 12 COSC1125 City Campus
Intelligent Web Systems 12 COSC1165 City Campus
Programming Techniques 12 COSC1283 City Campus
Algorithms and Analysis 12 COSC1285 City Campus
Advanced Programming 12 COSC1295 City Campus
Data Mining 12 COSC2111 City Campus
Advanced Programming Techniques 12 COSC2207 City Campus
Database Systems 12 COSC2407 City Campus
Programming Fundamentals 12 COSC2531 City Campus
Big Data Management 12 COSC2636 City Campus
Big Data Processing 12 COSC2637 City Campus
Case Studies in Data Science 12 COSC2669 City Campus
Practical Data Science with Python 12 COSC2670 City Campus
Social Media and Networks Analytics 12 COSC2671 City Campus
GIS Fundamentals 12 GEOM1159 City Campus
GIS Principles 12 GEOM1163 City Campus
Advanced GIS 12 GEOM2151 City Campus
GIS Analytics 12 GEOM2152 City Campus
Introduction to Information Security 12 INTE1120 City Campus
Case Studies in Cyber Security 12 INTE1122 City Campus
Information Theory for Secure Communications 12 INTE1128 City Campus
Information Systems Risk Management 12 INTE2396 City Campus
Knowledge and Data Warehousing 12 ISYS1072 City Campus
Web Search Engines and Information Retrieval 12 ISYS1078 City Campus
Data Visualisation and Communication 12 MATH2270 City Campus
Applied Bayesian Statistics 12 MATH2269 City Campus
Analysis of Categorical Data 12 MATH1298 City Campus
Design and Analysis of Experiments 12 MATH1302 City Campus
Forecasting 12 MATH1307 City Campus
Multivariate Analysis Techniques 12 MATH1309 City Campus
Regression Analysis 12 MATH1312 City Campus
Statistical Inference 12 MATH1315 City Campus
Statistics of Quality Control and Performance Analysis 12 MATH1316 City Campus
Stochastic Processes and Applications 12 MATH1317 City Campus
Time Series Analysis 12 MATH1318 City Campus
Game Theory and its Applications 12 MATH1320 City Campus
Methods and Models of Operations Research 12 MATH1326 City Campus
Questionnaire and Research Design 12 MATH2218 City Campus
Systems Simulation 12 MATH2219 City Campus
System Dynamics 12 MATH2220 City Campus
Sports Analytics 12 MATH2223 City Campus
Machine Learning 12 MATH2319 City Campus
Introduction to Statistical Computing 12 MATH1322 City Campus
 

*Top of page

Program transition plan

Not applicable. This is an exit point only from the Master of Statistics and Operations Research (MC004)

*Top of page
 
 
[Previous: Learning outcomes]