MKTG6010: Semester 1, 2021

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Units MKTG6010 Semester 1 2021 [Normal day]

Unit of study_

MKTG6010: Machine Learning in Marketing

Overview

With fast growth of the modern digital economy, multi-quintillion bytes of data is generated every day. This provides an enormous opportunity and a significant challenge for marketers to extract marketing insights because of not only the size of the data, but also the structure of the data. A growing proportion of the data is unstructured, such as customer emails and texts, mobile data, social media UGCs, C2C data on two-sided platforms in the sharing economy. Traditional marketing research methods cannot be used to solve these problems. This unit introduces state of the art machine learning methods to help marketers extract consumers insights from big data including structured and unstructured data and make better informed business decisions.

Details

Academic unit	Marketing
Unit code	MKTG6010
Unit name	Machine Learning in Marketing
Session, year ?	Semester 1, 2021
Attendance mode	Normal day
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	BUSS6002
Corequisites ?	None
Available to study abroad and exchange students	Yes

Teaching staff and contact details

Coordinator	Steven Lu, steven.lu@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Written exam	30%	Formal exam period	2 hours
Outcomes assessed:
Presentation	Presentation n/a	10%	Multiple weeks	15 minutes
Outcomes assessed:
Small continuous assessment	Class participation n/a	15%	Multiple weeks	As needed
Outcomes assessed:
Assignment	Term project 1 n/a	20%	Week 07	1500 words
Outcomes assessed:
Assignment	Term project 2 n/a	25%	Week 13	2000 words
Outcomes assessed:
= group assignment ? = Type C final exam ?

Term project stage 1

Term project stage 2

Presentation

In-semester assessment

Final exam:

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

Weekly schedule

WK	Topic	Learning activity
Week -02	Common Methods in Marketing Machine Learning	Lecture and tutorial (3 hr)
Week 01	Introduction to Marketing Machine Learning (MML)	Lecture and tutorial (3 hr)
Week 03	Modelling Choice Behaviour I	Lecture and tutorial (3 hr)
Week 04	Modelling Choice Behaviour II	Lecture and tutorial (3 hr)
Week 05	Modelling Choice Behaviour III	Lecture and tutorial (3 hr)
Week 06	Modelling Choice Behaviour IV	Lecture and tutorial (3 hr)
Week 07	Text and Voice Analytics for Marketing Applications I	Lecture and tutorial (3 hr)
Week 08	Text and Voice Analytics for Marketing Applications II	Lecture and tutorial (3 hr)
Week 09	Text and Voice Analytics for Marketing Applications III	Lecture and tutorial (3 hr)
Week 10	Marketing Network Analysis	Lecture and tutorial (3 hr)
Week 11	Image and Video Analytics for Marketing Applications	Lecture and tutorial (3 hr)
Week 12	Presentation	Lecture and tutorial (3 hr)
Week 13	Revision	Lecture and tutorial (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Apply machine learning methods commonly used in marketing and evaluate the advantages and disadvantages of those methods
LO2. Identify the machine learning models required to analyze practical problems in marketing
LO3. Apply machine learning to extract insights from big data for better decision making
LO4. Apply the required analytical techniques across marketing units

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Closing the loop

This is the first time this unit has been offered

Additional information

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Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

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