MC-PREDAN v.1 Master of Predictive Analytics
MPredAnylt(Curtin)
Course CRICOS Code: 092977C
Registered full-time Duration:
2 Years
Course Overview
Master Degrees (Coursework) prepare students to apply advanced knowledge for professional practice, scholarship and further learning corresponding to AQF level 9 qualifications.
This course is designed to prepare you for entry to the multi-disciplinary Predictive Data Analytics profession, in which many operations are automated and controlled remotely. Predictive Analytics is the study of data in order to predict and subsequently optimise management decisions. It has been developed in close collaboration with business and the resources industry to ensure the syllabus is comprehensive and will meet legal registration requirements. It provides a broad-based study of general data analytics including computing and business organisation, and then follows with three defined streams associated with the resources engineering industry, the business side of resources engineering productivity methodologies and the financial and investment industry
Career Opportunities
Business consultancy Accenture has forecast that 4000 workers (all involved with data analytics) are required by the end of 2016 in Australia for servicing the Liquefied Natural Gas (LNG) trains, rising to 9000 for the 21 LNG trains alone by 2018. ConocoPhillips is presently developing its Darwin LNG project using predictive analytics (LNG18, Perth). The Shell ‘Bridge’ project in the Gulf of Mexico automated 70% of a number of offshore platforms saving $50M over three years by reducing operations/maintenance/downtime, while the Curtin-Cisco Internet of Everything Innovation Centre (CIIC) was established in 2015 to develop research into the ability to apply predictive data analytics over the internet for optimised decision making by industry. So the Master's graduate may find employment in many areas of business including the oil and gas and mining industries, banking and finance depending on the stream selection.
Additional Course Expenses
Students may be expected to purchase a number of textbooks and other essential study materials.
Course Entry and Completion Details
Applicants for a Master Degree (Coursework) are required to meet University academic and English language entry standards; details are provided at http://futurestudents.curtin.edu.au. Subject to the duration of the course applicants usually require a Bachelor Degree or equivalent (and may require relevant work experience), Bachelor Honours Degree, Graduate Certificate or Graduate Diploma. Any specific course entry and completion requirements must also be met.
Specifically, students require a Bachelor's Degree.
Credit for Recognised Learning
Applications for credit towards a course are assessed on an individual basis. Credit reduces the amount of learning required to complete the course and may be granted for formal education qualifications, non-formal learning from non-award programs of study and informal learning through work experiences. Further information can be found at http://futurestudents.curtin.edu.au.
Intermediate Awards
A student who has successfully completed the requirements of an approved intermediate award may apply for graduation in that award subject to approval of Head of School/Department. Fees apply. Intermediate awards approved for this course:
GC-PREDAN Graduate Certificate in Predictive Analytics; GD-PREDAN Graduate Diploma in Predictive Analytics
Pathway to Further Study
Graduates may qualify for entry to Doctoral degrees. For further details, see the Graduate Research School website http://research.curtin.edu.au/postgraduate-research/future-research-students/entry-requirements/
Course Organisation
Master Degrees (Coursework) contain a series of units in a specialised area of study which may include compulsory (core), optional or elective units to cater for student preferences. They may also contain a range of majors/streams for students to choose from.
This two-year Master's course is in three stages with a number of options at each stage: 1. Graduate Certificate in Predictive Analytics providing basic concepts of data analysis, computing and visualisation over one semester. 2. This is followed by the Graduate Diploma in Predictive Analytics, which provides in-depth applications to data security, data mining and business to complete one year of study. 3. On successful completion of the Graduate Diploma, a student may take the second year Master’s program which specialises in one of three defined streams - Resource Operations Engineering, Asset Management and Productivity, and Finance and Investment Analysis.The Graduate Certificate course is designed for the part-time student who wishes to learn the basics of predictive data analytics. The Graduate Diploma course is designed to provide a student with an in-depth background to predictive analytics and the use of computing, while the Master’s course streams are designed to provide you with expertise in the engineering, business, or finance and investment fields of subsea engineering depending on the area of employment the student would wish to enter. Science and Engineering students may prefer to take the engineering or business streams whereas business and commerce graduates may prefer the business or finance streams. Flexibility is maintained with most students taking 150 credits of Core units with 50 credits of Optional units in the first year, and there are 25 credits of Core units and 125 credits of Optional Stream units in the second year. The result is a Master’s graduate who is an effective team member in the multi-disciplinary, multi-cultural industry of choice. Throughout the course, there will be extensive interaction with leading industry experts working in the three discipline areas.
Course Learning Outcomes
A graduate of this course can:
1. use research to apply an understanding of the theoretical background basis of data analytics and to allow the data processing of unstructured data, including all aspects of cluster analysis to produce a qualified interpretation of the data
2. analyse an unstructured data set or problem in a logical, rational and critical way; identify alternative methods of solving the issue and select the optimum solution that provides the best outcomes for both industry and the community
3. obtain, evaluate and apply relevant processing algorithms to unstructured data from a range of sources to solve or predict an operational problem prior to or during an occurrence
4. communicate effectively with a wide range of people from different discipline areas, professional positions and countries; communicate data analysis findings in a variety of ways via written, verbal or electronic communications
5. evaluate and utilise appropriate technology for the implementation of data analysis and prediction developments and the continual operational improvement of data generating systems throughout their lifecycle
6. appreciate the need for, and develop, a lifelong learning skills strategy in relation to enhanced personal and company performance
7. recognise the global nature of the predictive analytics industry and apply global standard practices and skills for acceptable prediction outcomes regardless of discipline or geographical location
8. practise appropriate industry data collection methodologies; work and apply discipline knowledge within the given social or industrial framework; with consideration of and respect for cultural diversity, indigenous perspectives and individual human rights
9. apply lessons learnt in a professional manner in all areas of prediction design, demonstrating leadership and ethical behaviour at all times
Duration and Availability
This course is 2 years' full-time or equivalent part-time study.
Location and delivery Mode
| Year | Location | Period | All* | Internal | Partially Online Internal^ | External | Fully Online# |
|---|---|---|---|---|---|---|---|
| 2017 | Bentley Campus | Semester 1 | Y |
The information displayed above refers to study periods and locations where the course is available for first time entry. Students are normally only offered or admitted to a course once.
* The course itself may not be available either solely internally or externally but individual units may be offered in either or both of those modes. Prospective students should contact the Course Coordinator for further information.
^ Course and associated units are offered in this mode permitting International Onshore student enrolment.
# Course and associated units are offered in this online only mode and DO NOT permit International Onshore student enrolment.
| Course Structure | Hrs/Wk | Credit | |||
|---|---|---|---|---|---|
| Year 1 Semester 1 | |||||
| ECOM5002 | v.1 | Quantitative Techniques for Business | 3.0 | 25.0 | |
| OR | |||||
| STAT5006 | v.1 ** | Statistical Data Analysis 1 | 3.0 | 12.5 | |
| OR | |||||
| COMP5005 | v.1 | Fundamentals of Programming | 4.0 | 25.0 | |
| OR | |||||
| COMP5006 | v.1 | Introduction to Computer Systems | 4.0 | 25.0 | |
| OR | |||||
| STAT5001 | v.1 * | Statistical Probability | 3.0 | 12.5 | |
| ISYS5007 | v.1 | Data Management | 3.0 | 25.0 | |
| ISEC5006 | v.1 | Fundamental Concepts of Data Security | 3.0 | 25.0 | |
| STAT5009 | v.1 | Decision Methods and Predictive Analytics | 3.0 | 25.0 | |
| 100.0 | |||||
| Year 1 Semester 2 | |||||
| STAT5006 | v.1 * | Statistical Data Analysis 1 | 3.0 | 12.5 | |
| OR | |||||
| ECOM5002 | v.1 | Quantitative Techniques for Business | 3.0 | 25.0 | |
| OR | |||||
| COMP5005 | v.1 | Fundamentals of Programming | 4.0 | 25.0 | |
| OR | |||||
| COMP5006 | v.1 | Introduction to Computer Systems | 4.0 | 25.0 | |
| OR | |||||
| STAT5001 | v.1 ** | Statistical Probability | 3.0 | 12.5 | |
| MGMT5022 | v.1 | Organisational Behaviour for Managers | 3.0 | 25.0 | |
| MEDA5003 | v.1 | Multidisciplinary Data Visualisation and Interpretation | 4.0 | 25.0 | |
| COMP5009 | v.1 | Data Mining | 3.0 | 25.0 | |
| 87.5 | |||||
| Year 2 Semester 1 | |||||
| STRP-ASMPR | v.1 | Asset Management and Productivity Stream | 200.0 | ||
| OR | |||||
| STRP-FIVAN | v.1 | Finance and Investment Analytics Stream | 200.0 | ||
| OR | |||||
| STRP-ROPEN | v.1 | Resource Operations Engineering Stream | 200.0 | ||
| 200.0 | |||||
* Students taking STAT5001, must take STAT5006.
** Students taking STAT5006, must take STAT5001.
Further Information
Course Structure Disclaimer
Curtin University reserves the right to alter the internal composition of any course to ensure learning outcomes retain maximum relevance. Any changes to the internal composition of a course will protect the right of students to complete the course within the normal timeframe and will not result in additional cost to students through a requirement to undertake additional units.
Disclaimer
Information in this publication is correct at the time of printing but may be subject to change.
In particular, the University reserves the right change the content and/or method of assessment, to change or alter tuition fees of any unit of study, to withdraw any unit of study or program which it offers, to impose limitations on enrolment in any unit or program, and/ or to vary arrangements for any program.
This material does not purport to constitute legal or professional advice.
Curtin accepts no responsibility for and makes no representations, whether express or implied, as to the accuracy or reliability in any respect of any material in this publication.
Except to the extent mandated otherwise by legislation, Curtin University does not accept responsibility for the consequences of any reliance which may be placed on this material by any person.
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International students
International students studying in Australia on a student visa can only study full-time and there are also specific entry requirements that must be met. As some information contained in this publication may not be applicable to international students, refer to international.curtin.edu.au for further information. Australian citizens, permanent residents and international students studying outside Australia may have the choice of full-time, part-time and external study, depending on course availability and in-country requirements.
