MC004 - Master of Statistics and Operations Research

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RMIT program code: MC004
Plan name: Master of Statistics and Operations Research
Plan code: MC004P12
Campus: City Campus
Credit points per semester:
Full time: 48
Part time: 24
CRICOS code: 074919G

Contact details and related links


2019 Enrolment Program Structure

To graduate you must complete the following:

  Year One of Program
 
AND  Year Two of Program
 

Important course offering information for the 2019 academic year:

Students can apply for the joint SAS and RMIT graduate certificate in "Advanced Analytics" upon completing the following 5 Math courses:

  • Introduction to Statistics (MATH 1324)
  • Data Pregrocessing (MATH2349)
  • Data Visualisation (MATH 2270)
  • Introduction to Statistical Computing (MATH1322)
  • Multivariate Analysis Techniques (MATH 1309)

PLUS only one of the following 3 Online SAS courses:

  • SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression
  • Applied Analytics using SAS Enterprise Miner
  • Predictive Modeling using Logistic Regression 

- Recommended full-time study load for this program is 48 credit points or Four (4) courses equivalent each semester.

- Total number of credit points needed to complete this master's degree program is 192 credit points or 16 courses equivalent.

 

Credit Transfer

At RMIT you can apply for credit so your previous learning or experience counts toward your RMIT program.  If you are eligible for credit transfer or advanced standing please complete Credit transfer and higher education recognition of prior learning application form.

 

Recognition of Prior Learning (RPL)

If you are seeking to claim RPL based on your work and professional development program offered by your employer (as well as previous formal study that is more than 10 years old) then there is an additional form (Higher Education application for recognition of prior learning (RPL) assessment document).

 

Lodge the relevant Credit Transfer/RPL form(s) on the RMIT Connect Student Portal as soon as possible along with all your supporting documents, e.g. transcript, resume, reference letters etc.

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Year One of Program

Important: Some core and elective courses are not offered every year. We strongly encourage you to check program information here or please contact the program manager via email for further assistance. Students may choose the order in which they undertake these courses.

New Students

Please select MATH2267 - Essential Mathematics if your current degree is not in the area of Science, Engineering, lacking the pre-requisite in mathematics or you have completed your bachelor degree more than ten years ago.

MATH2267 - Essential Mathematics is being removed as a core course. From Semester 2 2018, students can replace this with a program elective course. 

 

Complete the following Four (4) Courses:

Course Title

Credit Points

Prereqs/ Coreqs

Course Code

Campus

Semester 1 Class

Semester 2 Class

Mathematical Modelling and Decision Analysis12YesMATH1293City Campus1157
Introduction to Statistics12YesMATH1324City Campus1160 (FF)1154 (FF)
Database Concepts12YesISYS1055City Campus13391348
Data Preprocessing12YesMATH2349City Campus11401138
AND

Select and Complete Two (2) of the following Courses:

Course Title

Credit Points

Prereqs/ Coreqs

Course Code

Campus

Semester 1 Class

Semester 2 Class

Essential Mathematics12MATH2267City Campus11471141
Data Visualisation12YesMATH2270City Campus11411139
Forecasting12YesMATH1307City Campus1143
Regression Analysis12YesMATH1312City Campus1178
Statistical Inference12YesMATH1315City Campus1201
Statistics of Quality Control and Performance Analysis12YesMATH1316City Campus1199
Time Series Analysis12YesMATH1318City Campus1208
Questionnaire and Research Design12YesMATH2218City Campus1168
System Dynamics12YesMATH2220City Campus1210
Sports Analytics12YesMATH2223City Campus1183
Machine Learning12YesMATH2319City Campus1163
Introduction to Statistical Computing12MATH1322City Campus11591153
AND

Select and Complete Two (2) Courses from the Science Option list below:

 
AND

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Year Two of Program

Note: Some core and elective courses are not offered every year. We strongly encourage you to check program information here or please contact the program leader via email for further assistance.

Complete the following One (1) Course:

Course Title

Credit Points

Prereqs/ Coreqs

Course Code

Campus

Semester 1 Class

Semester 2 Class

Applied Research Project12YesMATH2191City Campus11351129
AND

Select and complete Sixty (60) Credit Points from the following Courses:

Course Title

Credit Points

Prereqs/ Coreqs

Course Code

Campus

Semester 1 Class

Semester 2 Class

Data Visualisation12YesMATH2270City Campus11411139
Forecasting12YesMATH1307City Campus1143
Regression Analysis12YesMATH1312City Campus1178
Statistical Inference12YesMATH1315City Campus1201
Statistics of Quality Control and Performance Analysis12YesMATH1316City Campus1199
Time Series Analysis12YesMATH1318City Campus1208
Minor Thesis24YesMATH1332City Campus11761161
Questionnaire and Research Design12YesMATH2218City Campus1168
System Dynamics12YesMATH2220City Campus1210
Sports Analytics12YesMATH2223City Campus1183
Machine Learning12YesMATH2319City Campus1163
Introduction to Statistical Computing12MATH1322City Campus11591153
AND

Select and Complete Two (2) Courses from the Science Option list below. Science Option Course List:

Course Title

Credit Points

Prereqs/ Coreqs

Course Code

Campus

Semester 1 Class

Semester 2 Class

Artificial Intelligence12YesCOSC1125City Campus1322
Algorithms and Analysis12YesCOSC1285City Campus13191316
Advanced Programming12YesCOSC1295City Campus13161313
Data Mining12YesCOSC2111City Campus1331
Database Systems12YesCOSC2407City Campus1341
Programming Fundamentals12YesCOSC2531City Campus13701371
Big Data Management12YesCOSC2636City Campus1325
Big Data Processing12YesCOSC2637City Campus1319
Case Studies in Data Science12YesCOSC2669City Campus1360
Practical Data Science12YesCOSC2670City Campus1367
Social Media and Networks Analytics12YesCOSC2671City Campus1335
GIS Fundamentals12GEOM1159City Campus1227 (FF)1215 (FF)
GIS Principles12YesGEOM1163City Campus1217
Advanced GIS12YesGEOM2151City Campus1211
GIS Analytics12YesGEOM2152City Campus1224
Introduction to Information Security12INTE1120City Campus1155
Case Studies in Cyber Security12INTE1122City Campus1138
Information Systems Risk Management12YesINTE2396City Campus1157
Web Search Engines and Information Retrieval12YesISYS1078City Campus1352
Data Visualisation12YesMATH2270City Campus11411139
Forecasting12YesMATH1307City Campus1143
Regression Analysis12YesMATH1312City Campus1178
Statistical Inference12YesMATH1315City Campus1201
Statistics of Quality Control and Performance Analysis12YesMATH1316City Campus1199
Time Series Analysis12YesMATH1318City Campus1208
Questionnaire and Research Design12YesMATH2218City Campus1168
System Dynamics12YesMATH2220City Campus1210
Sports Analytics12YesMATH2223City Campus1183
Machine Learning12YesMATH2319City Campus1163
Introduction to Statistical Computing12MATH1322City Campus11591153
 

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Contact details and related links

Program structure enquiries

College of Science, Engineering and Health
Academic Services Centre - Melbourne City Campus

Enquiries can be submitted via the RMIT Connect Student Portal 

Building 10, Level 9, Room 1
124 La Trobe Street
Melbourne VIC 3000
Tel: +61 3 9925 2621

 

Timetabling

Please go to the Class timetables web page to access timetabling information.

 

Material fees

Some courses and programs have material fees (fees for field trips, goods or services) associated with them. In most cases these charges are not compulsory, but are levied by the teaching school for materials they purchase on your behalf for use in your studies. Go to the Material Fees web page to check the material fees for your program and courses.

Enrolment

For more information about enrolment at RMIT University, please go to the Enrolment home page.

*Semester 1 classes generally commence in February and Semester 2 classes generally commence in July. Please contact your school for more information about specific class start days.

Policies

For more information about other RMIT University policies, please go to the Policies web site.

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