GD120 - Graduate Diploma in Statistics and Operations Research

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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

 

 

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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.

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 Preprocessing (MATH2349) which includes a Work Integrated Learning (WIL) experience in which your knowledge and skills are applied and assessed in a simulated workplace context.

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Program Structure

To graduate you must complete the following:
 

All courses listed may not be available each semester.

 

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Year One of Program

Complete the following Four (4) Courses:

Course Title

Credit Points

Course Code

Campus

Mathematical Modelling and Decision Analysis12MATH1293City Campus
Data Preprocessing12MATH2349City Campus
Introduction to Statistics12MATH1324City Campus
Database Concepts12ISYS1055City Campus
AND
Select and Complete Two (2) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Essential Mathematics12MATH2267City Campus
Data Visualisation12MATH2270City Campus
Applied Bayesian Statistics12MATH2269City Campus
Analysis of Categorical Data12MATH1298City Campus
Design and Analysis of Experiments12MATH1302City Campus
Forecasting12MATH1307City Campus
Multivariate Analysis Techniques12MATH1309City Campus
Regression Analysis12MATH1312City Campus
Statistical Inference12MATH1315City Campus
Statistics of Quality Control and Performance Analysis12MATH1316City Campus
Stochastic Processes and Applications12MATH1317City Campus
Time Series Analysis12MATH1318City Campus
Game Theory and its Applications12MATH1320City Campus
Methods and Models of Operations Research12MATH1326City Campus
Questionnaire and Research Design12MATH2218City Campus
Systems Simulation12MATH2219City Campus
System Dynamics12MATH2220City Campus
Sports Analytics12MATH2223City Campus
Machine Learning12MATH2319City Campus
Introduction to Statistical Computing12MATH1322City Campus
AND
Select and Complete Two (2) of the following Courses:

Course Title

Credit Points

Course Code

Campus

Scripting Language Programming12COSC1092City Campus
Artificial Intelligence12COSC1125City Campus
Intelligent Web Systems12COSC1165City Campus
Programming Techniques12COSC1283City Campus
Algorithms and Analysis12COSC1285City Campus
Advanced Programming12COSC1295City Campus
Data Mining12COSC2111City Campus
Advanced Programming Techniques12COSC2207City Campus
Database Systems12COSC2407City Campus
Programming Fundamentals12COSC2531City Campus
Big Data Management12COSC2636City Campus
Big Data Processing12COSC2637City Campus
Case Studies in Data Science12COSC2669City Campus
Practical Data Science12COSC2670City Campus
Social Media and Networks Analytics12COSC2671City Campus
GIS Fundamentals12GEOM1159City Campus
GIS Principles12GEOM1163City Campus
Advanced GIS12GEOM2151City Campus
GIS Analytics12GEOM2152City Campus
Introduction to Information Security12INTE1120City Campus
Case Studies in Cyber Security12INTE1122City Campus
Information Theory for Secure Communications12INTE1128City Campus
Information Systems Risk Management12INTE2396City Campus
Knowledge and Data Warehousing12ISYS1072City Campus
Web Search Engines and Information Retrieval12ISYS1078City Campus
Data Visualisation12MATH2270City Campus
Applied Bayesian Statistics12MATH2269City Campus
Analysis of Categorical Data12MATH1298City Campus
Design and Analysis of Experiments12MATH1302City Campus
Forecasting12MATH1307City Campus
Multivariate Analysis Techniques12MATH1309City Campus
Regression Analysis12MATH1312City Campus
Statistical Inference12MATH1315City Campus
Statistics of Quality Control and Performance Analysis12MATH1316City Campus
Stochastic Processes and Applications12MATH1317City Campus
Time Series Analysis12MATH1318City Campus
Game Theory and its Applications12MATH1320City Campus
Methods and Models of Operations Research12MATH1326City Campus
Questionnaire and Research Design12MATH2218City Campus
Systems Simulation12MATH2219City Campus
System Dynamics12MATH2220City Campus
Sports Analytics12MATH2223City Campus
Machine Learning12MATH2319City Campus
Introduction to Statistical Computing12MATH1322City Campus
 

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Program transition plan

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

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