MC004 - Master of Statistics and Operations Research

Plan: MC004P12 - Master of Statistics and Operations Research
Campus: City Campus

Program delivery and structure

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

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Approach to learning and assessment

The teaching approach in this program is designed to foster your development as an independent learner so that you will be able to extend your capabilities once you graduate. The teaching method includes lectorial, seminar, tutorial, workshop, practical and laboratory sessions, site visits and the provision of online materials. You will be expected to complete all prescribed out-of-class learning activities in preparation for scheduled face-to-face and online classes and encouraged to extend your learning through additional recommended readings and online activities. Of particular importance is the time spent in practice, laboratory based and work integrated learning activities that will develop your employability skills and capabilities. 

Several courses in the program are delivered online, rather than on-campus, and you are likely to find that other courses transition to online delivery as you progress through the program. All courses use Canvas for electronic provision of course material, tutorial problems and/or other relevant documents.   

Assessments in this program are designed to give you the opportunity to demonstrate your capabilities. Various forms of assessment will be used throughout the program since the assessment you undertake will be appropriate to the objectives and student learning outcomes for each course.  Assessment may include class tests, quizzes, essays/reports, oral class presentations, group projects, research projects, laboratory projects, practical assignments, timed assessment.  

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.

Work Integrated Learning (WIL) courses include:

MATH2349 Data Wrangling

MATH2191 Applied Research Project

These courses include a Work Integrated Learning experience in which your knowledge and skills will be applied and assessed in a real or simulated workplace context and where feedback from industry and/or community is integral to your experience.

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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 Optimisation for Decision Making 12 MATH2468 City Campus Applied Analytics 12 MATH1324 City Campus Database Concepts 12 ISYS1055 City Campus Data Wrangling 12 MATH2349 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 Advanced Optimisation 12 MATH1326 City Campus Questionnaire and Research Design 12 MATH2218 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 Statistical Data Science 12 MATH2472 City Campus
AND
Select and Complete Two (2) Courses from the Option Course list below:
AND
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Year Two of Program
Complete the following One (1) Course:
Course Title Credit Points Course Code Campus Applied Research Project 12 MATH2191 City Campus
AND
Select and Complete Sixty (60) Credit points from the following Courses:
Course Title Credit Points Course Code 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 Advanced Optimisation 12 MATH1326 City Campus Minor Thesis 24 MATH1332 City Campus Questionnaire and Research Design 12 MATH2218 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 Statistical Data Science 12 MATH2472 City Campus
AND
Select and Complete Two (2) Courses from the Option Course list below:
AND
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Option Course List:
List of Option 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 Cloud Computing 12 COSC2640 City Campus Practical Data Science with Python 12 COSC2670 City Campus Social Media and Networks Analytics 12 COSC2671 City Campus Deep Learning 12 COSC2779 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 Managing Semi-structured and Unstructured Data 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 Advanced Optimisation 12 MATH1326 City Campus Questionnaire and Research Design 12 MATH2218 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 Statistical Data Science 12 MATH2472 City Campus
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Program transition plan

Transition Plan sem 1, 2024 

Minor amendment have been made to your program effective semester 1, 2024. These amendments will not impact your enrolment or completion of the program.  

Details of amendment: 

1) Title change for MATH1326: 

• Old title: Advanced Optimisation with Python 
• New title: Advanced Optimisation 

2) Course code change for Optimisation for Decision Making: 

• Old code: MATH1293 
• New code: MATH2468

Transition plan sem 1, 2019

Below listed courses have been removed from the program or were  moved from core courses list to Options list. If you have successfully completed any of the courses listed below, your credits will be counted towards your overall program:

- MATH2267 - Essential Mathematics - Moved from core to Options

- MATH1319 – Analysis of Large Data Sets - removed from the program

- COSC2668 Data Visualisation and Communication - removed from the program

Transition plan sem 1, 2018

As a part of our continuous improvement efforts we are updating the program structure of your program effective Semester 1, 2018 as follows:

• MATH1322 – Introduction to Statistical Computing will be removed from the core and replaced by a new course, MATH2349 Data Preprocessing. MATH1322 will be changed to an option course. 
• COSC2668 Data Visualisation and Communication will be removed from the options as it will no longer be offered.
• MATH1319 – Analysis of Large Data Sets will be replaced with MATH2319 – Machine Learning due to substantial content overlap.

These changes will impact students who have enrolled prior to 2018. Please use the following program transition plan to ensure you continue to enrol in the appropriate courses. If you have any doubts, please contact the Program Manager, Dr Mali Abdollahian (mali.abdollahian@rmit.edu.au).

The following plan assumes you have NOT been granted exemptions and or credits for the full 192 credit point program. If you have been granted exemptions/credits and you are unclear about how the transition plan applies to you, please contact the Program Manager, Dr Mali Abdollahian (mali.abdollahian@rmit.edu.au).

Courses completed during previous program structures will be recognised. As of Semester 1, 2018, you will need to follow the new plan. 
However, you may not be able to complete all the required courses under the new plan, OR your previous completion of other courses may preclude you from the courses listed in the new plan. 

Therefore, the following plan will explain how to combine the new and old plans. If you’re in doubt, please contact the Program Manager, Dr Mali Abdollahian (mali.abdollahian@rmit.edu.au).

Students enrolled before 2018 and who plan to graduate in 2018 or beyond must satisfy the following minimum requirements. You must complete the four (4) following courses:

• MATH2267 - Essential Mathematics 
• MATH1324 - Introduction to Statistics 
• ISYS1055 - Database Concepts
• MATH1293 - Mathematical Modelling and Decision Analysis

Please keep in mind that depending on your background and with the permission of the Program Manager, you may have been permitted to swap one or more of these courses for more advanced MATH option courses. 
You must have completed at least one (1) of the following courses:

• MATH2349 – Data Preprocessing OR MATH1322 – Introduction to Statistical Computing

You must also complete the following course in your 2nd year of the program (Full-time equivalent):

• MATH2191 – Applied Research Project.

The full 192 credit point program may include up to an additional six (6) MATH option courses (depending on how many courses you completed above). Please keep in mind that MATH1332 – Minor Thesis counts as two MATH courses (24 credit points). 
The remaining four (4) courses (48 credit points) can be either additional MATH option courses or other option courses from the approved list. 

If you have previously completed MATH1319 – Analysis of Large Datasets, this will still count as one of your other MATH courses. As there will be substantial overlap between this course and MATH2319 – Machine Learning, please seek advice from the MATH2319 course coordinator before choosing to enrol.

Students commencing from Semester 1, 2018 onwards will need to follow the new program structure. 
If in doubt, please contact the Program Manager, Dr Mali Abdollahian (mali.abdollahian@rmit.edu.au).

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