Predictive Analytics

Subject MGMT90216 (2016)

Note: This is an archived Handbook entry from 2016.

Credit Points: 6.25
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2016:

October, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period 13-Oct-2016 to 14-Oct-2016
Assessment Period End 15-Nov-2016
Last date to Self-Enrol 13-Oct-2016
Census Date 21-Oct-2016
Last date to Withdraw without fail 04-Nov-2016

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 16 hours
Total Time Commitment: Not available
Study Period Commencement:
Credit Points:
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support:


Subject Overview:

Predicting key business and economic variables is increasingly important, as it drives both objective decision-making and improved profitability. This course aims to cover the basic forecasting methods used to predict business and economic variables, based on historical data. These include traditional regression, time series, as well as emerging methods such as ensemble forecasts. Throughout, the focus will be on practical implementation of forecasting techniques using the publicly available software “R”. The importance of benchmarking, the assessment of forecasts from different models, and the use of forecasts in decision-making frameworks, will also be highlighted.

Learning Outcomes:

On completion of this subject, students should be able to demonstrate;

  • an understanding of a range of models relevant to forecasting time series data.
  • the skills to apply appropriate modelling and forecasting techniques in the “R” software to business and economic contexts, and to critique and compare competing methodologies.
  • the skills to translate forecasting outputs to information and provide recommendation for relevant business problems.
  • Multiple-choice on the different predictive methods covered in the subject (30 mins) to be completed at the end of the second day (20%).
  • Essay – develop a recommendation for operationalizing predictive methods in a business case, and how they can be used to improve decision-making (2000 words) due four weeks after the class (80%).
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Related Course(s): Specialist Certificate in Strategic Marketing

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