Outline

Overview

This unit introduces students to the strategic use of data in modern business decision-making. Exploring five essential analytical dimensions—descriptive (what happened), diagnostic (why it happened), predictive (what may happen), prescriptive (how to make it happen), and emerging analytics (the evolving field of analytics) —the unit provides a comprehensive foundation in data analysis methodologies. Designed for those new to data-driven decision making, students will learn to effectively describe, analyse, evaluate, visualise, and communicate data in contemporary business contexts to enhance managerial decision-making. The curriculum balances theoretical frameworks with practical applications, offering both conceptual understanding and hands-on experience with industry-standard tools through structured, interactive workshops. No programming knowledge is required, though familiarity with Excel is necessary. This practical, industry-relevant approach equips students with immediately applicable skills for translating raw data into actionable business intelligence, positioning them to make more informed strategic decisions in today's data-rich business environment.

Unit details and rules

Academic unit	Finance
Credit points	6
Prerequisites ?	None
Corequisites ?	DMBA6001
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Andrew Grant, andrew.grant@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Research Design: Addressing Complex Data Challenges Reflecting on datasets from weeks 1–3, students will propose a research question and map out the data processing steps, tools, and strategies needed to resolve it, detailing the practical applicability of these techniques.	30%	Week 04 Due date: 30 Aug 2026 at 23:59 Closing date: 09 Sep 2026	1500 words	AI allowed
Outcomes assessed:
Presentation	Group Proposal: Industry Context & Analytical Strategy In Week 6, groups will present their project proposal: an industry context, chosen datasets, and proposed analytical/visualization methods. Participants must justify their approach to receive feedback from educators and peers.	20%	Week 06 Due date: 13 Sep 2026 at 23:59 Closing date: 23 Sep 2026	up to 20 mins (in group of 4)	AI allowed
Outcomes assessed:
Written work	Group Report This report accompanies the presentation	20%	Week 06 Due date: 13 Sep 2026 at 23:59 Closing date: 23 Sep 2026	1200 word (or equivalent in diagrams/tab	AI allowed
Outcomes assessed:
Presentation	Individual Presentation + Q&A component This is a presentation of the group project in which each student will present an aspect of their finalised group project from week 6.	30%	Week 09 Due date: 18 Oct 2026 at 23:59 Closing date: 28 Oct 2026	5 mins + 5 mins Q&A	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Research Design: Students will propose possible research questions that the complex data sets introduced in weeks 1 to 3 could potentially address. They will reflect on the manipulations they would have to perform on the data, the tools they would need to use, various pathways to address the problem and the challenges associated with these. What techniques can be used in their work contexts?

Presentation: Each group will identify an industry context (a sector, or an individual company), data sets (either publicly available or provided by students) and potential business questions or problems that the data sets could potentially provide insight into. A draft of the proposed context, analysis methods and visualisation methods, with justification, will be presented by the group live in Week 6 for feedback from the educators and class.

Group Report: This report accompanies the presentation.

Presentation and Q&A: Each student will present an aspect of their finalised written project from week 6. This will be followed by Q&A.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

As per University Policy

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 Data Analytics in a Digital Economy	Workshop (2 hr)
Week 01	Introduction to Data Analytics in a Digital Economy	Self-directed learning (2 hr)
Week 02	Data Architecture and Pipeline	Workshop (2 hr)
Week 02	Data Architecture and Pipeline	Self-directed learning (2 hr)
Week 03	Data Processing	Workshop (2 hr)
Week 03	Data Processing	Self-directed learning (2 hr)
Week 04	Data Transformation	Workshop (2 hr)
Week 04	Data Transformation	Self-directed learning (2 hr)
Week 05	Data Visualisation part 1	Workshop (2 hr)
Week 05	Data Visualisation part 1	Self-directed learning (2 hr)
Week 06	Data Visualisation part 2	Workshop (2 hr)
Week 06	Data Visualisation part 2	Self-directed learning (2 hr)
Week 07	Data Prediction Modelling	Workshop (2 hr)
Week 07	Data Prediction Modelling	Self-directed learning (2 hr)
Week 08	Data-Driven Decision-Making	Workshop (2 hr)
Week 08	Data-Driven Decision-Making	Self-directed learning (2 hr)
Week 09	Frontier of Data Analytics	Workshop (2 hr)
Week 09	Frontier of Data Analytics	Self-directed learning (2 hr)
Week 10	Unit Review & Presentation	Workshop (2 hr)
Week 10	Unit Review & Presentation	Self-directed learning (2 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. Differentiate between descriptive, diagnostic, predictive, prescriptive, and emerging analytic approaches and identify appropriate applications for each in business contexts
LO2. Design effective data visualisations that communicate complex information clearly to diverse stakeholders
LO3. Apply analytical frameworks and methodologies to transform raw data into actionable business intelligence
LO4. Analyse and interpret business data using industry-standard tools to generate meaningful insights for strategic decision-making processes.

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.

This unit is being offered for the first time in 2026.

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

DMBA6007: Data Analytics for Strategic Decision Making

Semester 2, 2026 [Online] - 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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links