Outline

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.

Unit details and rules

Academic unit	Marketing
Credit points	6
Prerequisites ?	BUSS6002 or QBUS5011
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jiang Qian, jiang.qian@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Contribution	Class participation n/a	15%	Multiple weeks	As needed	AI allowed
Outcomes assessed:
Data analysis	Final individual assignment Final individual assignment	30%	STUVAC	1000 words	AI allowed
Outcomes assessed:
Data analysis	Term project 1 n/a	20%	Week 07	1500 words	AI allowed
Outcomes assessed:
Presentation	Presentation n/a	10%	Week 12	15 minutes	AI allowed
Outcomes assessed:
Data analysis	Term project 2 n/a	25%	Week 13	2000 words	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Term project 1: A real-world marketing problem will be assigned. As a group you are expected to analyse the data provided, using various machine learning techniques you learn in the first half of the courese.

Term project 2 & presentation : A real-world marketing problem will be assigned. As a group you are expected to analyse the data provided, using various machine learning techniques you learn in the second half of the courese. You are also expected to clearly present your analysis and results to relevant stakeholders.

Class participation: To promote a learning and knowledge sharing atmosphere, students are required to prepare before class and participate actively in class. Marks awarded are based on students’ preparation and participation.

Final individual assignment: Students need to apply the knowledge learned to a given problem, case study and/ or series of questions. This final individual task will assess the students' understanding of the subject materials delivered over the entire semester and their ability to apply this information.

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 guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Marketing Machine Learning (MML)	Lecture (2 hr)
Week 01	Introduction to Marketing Machine Learning (MML)	Tutorial (1 hr)
Week 02	Common Methods in Marketing Machine Learning	Lecture (2 hr)
Week 02	Common Methods in Marketing Machine Learning	Tutorial (1 hr)
Week 03	Modelling Choice Behaviour I	Lecture (2 hr)
Week 03	Modelling Choice Behaviour I	Tutorial (1 hr)
Week 04	Modelling Choice Behaviour II	Lecture (2 hr)
Week 04	Modelling Choice Behaviour II	Tutorial (1 hr)
Week 05	Modelling Choice Behaviour III	Lecture (2 hr)
Week 05	Modelling Choice Behaviour III	Tutorial (1 hr)
Week 06	Modelling Choice Behaviour IV	Lecture (2 hr)
Week 06	Modelling Choice Behaviour IV	Tutorial (1 hr)
Week 07	Text and Voice Analytics for Marketing Applications I	Lecture (2 hr)
Week 07	Text and Voice Analytics for Marketing Applications I	Tutorial (1 hr)
Week 08	Text and Voice Analytics for Marketing Applications II	Lecture (2 hr)
Week 08	Text and Voice Analytics for Marketing Applications II	Tutorial (1 hr)
Week 09	Text and Voice Analytics for Marketing Applications III	Lecture (2 hr)
Week 09	Text and Voice Analytics for Marketing Applications III	Tutorial (1 hr)
Week 10	Marketing Network Analysis	Lecture (2 hr)
Week 10	Marketing Network Analysis	Tutorial (1 hr)
Week 11	Image and Video Analytics for Marketing Applications	Lecture (2 hr)
Week 11	Image and Video Analytics for Marketing Applications	Tutorial (1 hr)
Week 12	Presentation	Lecture (2 hr)
Week 12	Presentation	Tutorial (1 hr)
Week 13	Revision	Lecture (2 hr)
Week 13	Revision	Tutorial (1 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.

Required readings

Steven Lu (2023), Machine Learning in Marketing, Pearson Originals (Print ISBN 9780655796571; eBook ISBN 9780655706588).

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

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

A prescribed textbook has been added.

Additional information

Business School Policies

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

MKTG6010: Data Analytics in Marketing

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links