MRKT20056 - Digital Marketing and Machine Learning

General Information

Unit Synopsis

Digital marketing is fundamentally crucial to any business success nowadays, and has become every marketer’s Best Friend Forever (BFF). In this unit, you will refresh your mindset with a wide range of essential digital marketing concepts and theories, and will turbocharge your skillset in relation to web, search, content, social media, and mobile marketing. You will also learn how advancements in marketing technology in particular machine learning are revolutionising marketing practices and enabling smarter marketing. This unit aims to ultimately take your marketing expertise to the next level.

Details

Level Postgraduate
Unit Level 9
Credit Points 6
Student Contribution Band SCA Band 4
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Pre-requisite: MRKT20052.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 2 - 2025

Term 2 - 2025 Profile
Brisbane
Melbourne
Online
Sydney

Attendance Requirements

All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

This information will not be available until 8 weeks before term.
To see assessment details from an earlier availability, please search via a previous term.

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University's Grades and Results Policy for more details of interim results and final grades

Past Exams

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Previous Feedback

Term 2 - 2023 : The overall satisfaction for students in the last offering of this course was 92.31% (`Agree` and `Strongly Agree` responses), based on a 33.33% response rate.

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Source: Self observation
Feedback
Digital marketing is now fundamentally crucial to the success of any strategic marketing programs.
Recommendation
The teaching staff in the future offerings of this unit will be encouraged to utilise more learning and teaching materials related to digital marketing.
Action Taken
This recommendation has been implemented.
Source: Staff reflection
Feedback
Machine learning has emerged as an important force in revolutionising practices related to strategic marketing or marketing metrics and analytics.
Recommendation
The teaching staff in the future offerings of this unit will be encouraged to incorporate the learning and teaching materials related to the application of machine learning to marketing.
Action Taken
This recommendation has been implemented.
Source: Student feedback.
Feedback
Positive comments about the teaching staff involved in delivering this unit.
Recommendation
The teaching staff in the future offerings of this unit will be encouraged to keep delivering the unit contents in an effective, supportive, and engaging manner.
Action Taken
In Progress
Source: Staff self-reflection.
Feedback
The designing of assessments in this unit has been based on reaching a balance of involving valuable theoretical aspects and a high level of relevancy to the real-world scenarios.
Recommendation
The teaching staff in the future offerings of this unit will be encouraged to continue the practice of designing assessments that integrate critical theoretical aspects and are highly relatable to real-world contexts.
Action Taken
In Progress
Unit learning Outcomes
This information will not be available until 8 weeks before term.
To see Learning Outcomes from an earlier availability, please search via a previous term.