MC242 - Master of Analytics

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Plan: MC242 - Master of Analytics
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 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.

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

Work Integrated Learning (WIL) courses include:

MATH2349 Data Wrangling

In this course you will develop and apply your data preprocessing skills to complex, noisy, and inconsistent real world data using leading open source software.

MATH2191 Applied Research Project. 

This course includes 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 Three (3) Courses:

Course Title

Credit Points

Course Code

Campus

Applied Analytics 12 MATH1324 City Campus
Database Concepts 12 ISYS1055 City Campus
Data Wrangling 12 MATH2349 City Campus
AND
Select and Complete up to 24 credit points from the following Courses (please note: if you choose 24 credit points here you may only choose 12 credit points in the second list):

Course Title

Credit Points

Course Code

Campus

Essential Mathematics 12 MATH2267 City Campus
Data Visualisation and Communication 12 MATH2270 City Campus
Machine Learning 12 MATH2319 City Campus
Time Series Analysis 12 MATH1318 City Campus
AND
Select and Complete up to 24 credit points from the following Courses (please note: if you chose 24 credit points in the above list then you may only choose 12 credit points in this list):

Course Title

Credit Points

Course Code

Campus

Optimisation for Decision Making 12 MATH2468 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
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
Introduction to Statistical Computing 12 MATH1322 City Campus
Statistical Data Science 12 MATH2472 City Campus
AND
Select and Complete Two (2) Courses from the Science 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 Two (2) of the following courses not previously completed:

Course Title

Credit Points

Course Code

Campus

Data Visualisation and Communication 12 MATH2270 City Campus
Machine Learning 12 MATH2319 City Campus
Time Series Analysis 12 MATH1318 City Campus
Essential Mathematics 12 MATH2267 City Campus
AND
Select and Complete Thirty-Six (36) credit points from the following Courses:

Course Title

Credit Points

Course Code

Campus

Optimisation for Decision Making 12 MATH2468 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
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
Introduction to Statistical Computing 12 MATH1322 City Campus
Statistical Data Science 12 MATH2472 City Campus
AND
Select and Complete Two (2) Courses from the Science Option course list below. Science Option Course List:

Course Title

Credit Points

Course Code

Campus

Accounting for Management Decisions 12 ACCT2127 City Campus
Corporate Finance 12 BAFI1059 City Campus
Fixed Income Securities and Credit Analysis 12 BAFI1065 City Campus
Financial Decision Making 12 BAFI1100 City Campus
Options, Futures and Risk Management 12 BAFI2081 City 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
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
Deep Learning 12 COSC2779 City Campus
Quantitative Methods in Finance 12 ECON1095 City Campus
Economic Analysis for Business 12 ECON1113 City Campus
Financial Econometrics 12 ECON1195 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
Digital Risk Management and Information Security 12 INTE1002 City Campus
Digital Strategy 12 INTE1030 City Campus
Business Intelligence 12 INTE1040 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
e Procurement and Supply Chain Technologies 12 INTE1208 City Campus
e Business Models and Issues 12 INTE1214 City Campus
Information Systems Risk Management 12 INTE2396 City Campus
Decision Support Systems 12 ISYS1018 City Campus
Knowledge and Data Warehousing 12 ISYS1072 City Campus
Managing Semi-structured and Unstructured Data 12 ISYS1078 City Campus
Globalization and Business IT 12 ISYS2394 City Campus
Business Systems Analysis and Design 12 ISYS2395 City Campus
Intelligent Enterprise Systems 12 ISYS2396 City Campus
Risk Management and Feasibility 12 MANU1051 City Campus
Engineering Economic Strategy 12 MANU1054 City Campus
Planning and Control 12 MANU1378 City Campus
Sustainable Engineering Systems and Environment 12 MANU1381 City Campus
Measurement and Improvement 12 MANU1474 City Campus
Project Management 12 MANU2123 City Campus
Optimisation for Decision Making 12 MATH2468 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
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
Introduction to Statistical Computing 12 MATH1322 City Campus
Marketing Management 12 MKTG1100 City Campus
Consumer Behaviour 12 MKTG1101 City Campus
Interactive Marketing 12 MKTG1105 City Campus
Services Marketing 12 MKTG1112 City Campus
Business and Network Marketing 12 MKTG1209 City Campus
Supply Chain Principles 12 OMGT1021 City Campus
Supply Chain Modelling & Design 12 OMGT2087 City Campus
Supply Chain Sustainability 12 OMGT2190 City Campus
Strategic Operations and Supply Chain Management 12 OMGT2191 City Campus
e Business Supply Chains 12 OMGT1236 City Campus
Distribution and Freight Logistics 12 OMGT1012 City Campus
Statistical Data Science 12 MATH2472 City Campus
 

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Program transition plan

Transition plan sem 1, 2024

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

Details of amendment: 

1) MATH1326 has had a title change with no change to content: 

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

2) Optimisation for Decision Making has a new course code with no change to title or content: 

  • Old code: MATH1293
  • New code: MATH2468 

3) ECON1238 Econometric Techniques removed from the Option list. If you’ve already completed this course, credit achieved will count towards completion of your program. 

Transition plan sem 1, 2019

As a part of our continuous improvement efforts we are updating the program structure of your program effective Semester 1, 2019 as follows:

MATH2267 - Essential Mathematics was removed from the core list and added to the Options list. If you have successfully completed this course prior to sem 1, 2019, credit for this course will be counted towards your overall 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. 
  • MATH1293 – Mathematical Modelling and Decision Analysis will be removed from the core, but retained as an option.
  • Three existing courses, MATH2270 – Data Visualisation, MATH2319 – Machine Learning and MATH1328 Time Series Analysis will be added to the core structure for either 1st and 2nd year (Full time equivalent). 
  • COSC2668 Data Visualisation and Communication will be removed from the options as it will no longer be offered. Students in COSC2668 will be transferred into MATH2270.
  • 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 196 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 three (3) following courses:

  • MATH2267 - Essential Mathematics 
  • MATH1324 - Introduction to Statistics 
  • ISYS1055 - Database Concepts

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

AND
You must have completed at least one (1) of the following courses:

  • MATH1293 – Mathematical Modelling and Decision Analysis OR MATH2270 Data Visualisation OR MATH2319 - Machine Learning OR MATH1318 - Time Series Analysis

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

  • MATH2191 – Applied Research Project.

The full 196 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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