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MIS772 - Predictive Analytics


2020 unit information

Important Update:

Classes and seminars in Trimester 2, 2020 will be online.

Last updated: 2 June 2020

Enrolment modes:Trimester 1: Burwood (Melbourne), Cloud (online)
Trimester 2: Burwood (Melbourne), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Jacob Cybulski
Trimester 2: Jacob Cybulski

MIS770 or MIS770A* for M722, M751, M761, M661, M755 and S777 students ONLY

Incompatible with: Nil
Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus:

1 x 2 hour class, 1 x 1 hour computer lab per week

Scheduled learning activities - cloud:11 x 2 hour class (livestreamed with recordings provided) + 11 x 1 hour online seminar/workshop


*Students who are either enrolled in or have completed MIS770A - please contact a student adviser buslaw@deakin.edu.au


The ‘information age’ has combined with the widespread adoption of digital technology to turn information into a key business asset.  Businesses and governments now have access to massive volumes of data and require skills and expertise in making sense of this information for strategic decision making. This unit will provide students with the knowledge and skills to build predictive models and use data mining tools with ‘Big Data’. Students will be given the opportunity to gain hands-on experience with one of the most widely used predictive analytics software tools globally.


These are the Learning Outcomes (ULO) for this Unit
At the completion of this Unit, successful students can:

Deakin Graduate Learning Outcomes


Explain and critique major statistical theories and data mining concepts.

GLO1: Discipline-specific knowledge and capabilities


Critically evaluate and build predictive analytics solutions for real-world requirements.

GLO1: Discipline-specific knowledge and capabilities


Analyse multifaceted business problems, and subsequently propose, construct and evaluate analytic solutions using a combination of predictive techniques and methods.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy


These Unit Learning Outcomes are applicable for all teaching periods throughout the year


Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1 (Individual) - Develop predictive models for a business 2000 words 20% Information not yet available
Assessment 2 (Individual) - Develop advanced predictive models for a business 3000 words 30% Information not yet available
Examination 2 hours 50% Exam period

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Hurdle requirement

Hurdle requirement: achieve at least 50% of the marks available on the examination

Learning Resource

The texts and reading list for the unit can be found on the University Library via the link below: MIS772 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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