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 Master of Analytics 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 teams 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 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 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.
  • The Applied Research Project (MATH2191) will provide practice in the application of theory, and 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.

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

Work Integrated Learning (WIL) courses include:

MATH2349 Data Preprocessing

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:
 

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Year One of Program

Complete the following Three (3) Courses:

Course Title

Credit Points

Course Code

Campus

Introduction to Statistics12MATH1324City Campus
Database Concepts12ISYS1055City Campus
Data Preprocessing12MATH2349City 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 Mathematics12MATH2267City Campus
Data Visualisation12MATH2270City Campus
Machine Learning12MATH2319City Campus
Time Series Analysis12MATH1318City 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 this list):

Course Title

Credit Points

Course Code

Campus

Mathematical Modelling and Decision Analysis12MATH1293City 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
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
Introduction to Statistical Computing12MATH1322City 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 Project12MATH2191City Campus
AND
Select and Complete Two (2) of the following courses not previously completed:

Course Title

Credit Points

Course Code

Campus

Data Visualisation12MATH2270City Campus
Machine Learning12MATH2319City Campus
Time Series Analysis12MATH1318City Campus
Essential Mathematics12MATH2267City Campus
AND
Select and Complete Thirty-Six (36) credit points from the following Courses:

Course Title

Credit Points

Course Code

Campus

Mathematical Modelling and Decision Analysis12MATH1293City 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
Game Theory and its Applications12MATH1320City Campus
Methods and Models of Operations Research12MATH1326City Campus
Minor Thesis24MATH1332City Campus
Questionnaire and Research Design12MATH2218City Campus
Systems Simulation12MATH2219City Campus
System Dynamics12MATH2220City Campus
Sports Analytics12MATH2223City Campus
Introduction to Statistical Computing12MATH1322City 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 Decisions12ACCT2127City Campus
Corporate Finance12BAFI1059City Campus
Fixed Income Securities and Credit Analysis12BAFI1065City Campus
Financial Decision Making12BAFI1100City Campus
Options, Futures and Risk Management12BAFI2081City 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
Quantitative Methods in Finance12ECON1095City Campus
Economic Analysis for Business12ECON1113City Campus
Financial Econometrics12ECON1195City Campus
Econometric Techniques12ECON1238City Campus
GIS Fundamentals12GEOM1159City Campus
GIS Principles12GEOM1163City Campus
Advanced GIS12GEOM2151City Campus
GIS Analytics12GEOM2152City Campus
Digital Risk Management and Information Security12INTE1002City Campus
Digital Strategy12INTE1030City Campus
Business Intelligence12INTE1040City Campus
Introduction to Information Security12INTE1120City Campus
Case Studies in Cyber Security12INTE1122City Campus
Information Theory for Secure Communications12INTE1128City Campus
e Procurement and Supply Chain Technologies12INTE1208City Campus
e Business Models and Issues12INTE1214City Campus
Information Systems Risk Management12INTE2396City Campus
Decision Support Systems12ISYS1018City Campus
Knowledge and Data Warehousing12ISYS1072City Campus
Web Search Engines and Information Retrieval12ISYS1078City Campus
Globalization and Business IT12ISYS2394City Campus
Business Systems Analysis and Design12ISYS2395City Campus
Enterprise Systems12ISYS2396City Campus
Risk Management and Feasibility12MANU1051City Campus
Engineering Economic Strategy12MANU1054City Campus
Planning and Control12MANU1378City Campus
Sustainable Engineering Systems and Environment12MANU1381City Campus
Measurement and Improvement12MANU1474City Campus
Project Management12MANU2123City Campus
Mathematical Modelling and Decision Analysis12MATH1293City 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
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
Introduction to Statistical Computing12MATH1322City Campus
Marketing Management12MKTG1100City Campus
Consumer Behaviour12MKTG1101City Campus
Interactive Marketing12MKTG1105City Campus
Services Marketing12MKTG1112City Campus
Business and Network Marketing12MKTG1209City Campus
Supply Chain Principles12OMGT1021City Campus
Supply Chain Modelling & Design12OMGT2087City Campus
Supply Chain Sustainability12OMGT2190City Campus
Strategic Operations and Supply Chain Management12OMGT2191City Campus
e Business Supply Chains12OMGT1236City Campus
Distribution and Freight Logistics12OMGT1012City Campus
 

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Program transition plan

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 James Baglin (james.baglin@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 James Baglin (james.baglin@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 James Baglin (james.baglin@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 James Baglin (james.baglin@rmit.edu.au).

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