Outline

Overview

Ever wondered how to turn data into dashboards that drive real decisions? This unit combines statistical thinking and visual storytelling to design dashboards that go beyond aesthetics - they deliver meaningful insights to real stakeholders. In this hands-on unit, you will work in a team to tackle a problem of your choice. You will source data, apply analytics, and build interactive dashboards. Your dashboard will bring key metrics, forecasts, and models to life - designed with your audience in mind. Throughout the unit, you will develop the skills to translate complex data into clear, actionable visuals that support decision-making across business, government, and not-for-profit settings. By the end of the unit, you will be able to create dashboards that are insightful, audience-focused, and grounded in statistical reasoning - ready for the kinds of conversations that happen in boardrooms and strategy sessions.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Familiarity with coding, e.g. in R or Python
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Jaslene Huan Lin, huan.lin@sydney.edu.au
Lecturer(s)	Jie Kang, jie.kang@sydney.edu.au

Assessment

The census date for this unit availability is 8 May 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Dashboard Critique & Redesign Assignment 1: Evaluation report	10%	Week 03 Due date: 10 May 2026 at 23:59 Closing date: 20 May 2026	3 pages	AI allowed
Outcomes assessed:
Written work	Dashboard Implementation Assignment 2: Dashboard implementation report	20%	Week 04 Due date: 17 May 2026 at 23:59 Closing date: 27 May 2026	2 pages and 1 dasboard app	AI allowed
Outcomes assessed:
Presentation	Group Project: Dashboard for Decision-Making Group presentation	45%	Week 07 Due date: 02 Jun 2026 at 23:59 Closing date: 12 Jun 2026	15 minutes	AI allowed
Outcomes assessed:
Written work	Group Project: Dashboard for Decision-Making Group project report	20%	Week 08 Due date: 14 Jun 2026 at 23:59 Closing date: 24 Jun 2026	600 words	AI allowed
Outcomes assessed:
Written work	Reflective Statement Self-reflection on the group project	5%	Week 08 Due date: 14 Jun 2026 at 23:59 Closing date: 24 Jun 2026	300 words	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

ODAT5032 includes three assessment types designed to support your learning: individual assignments, group project, and a self-reflection task.

Assignments: the two individual assignments are designed to test your understanding of specific topics covered in the unit and provide feedback on your progress.

Assignment 1: Students critically evaluate the effectiveness of poorly designed dashboards in supporting data-driven decision-making.
Assignment 2: Students design, implement and deploy a dashboard using a supplied dataset. Students create specified visualisations and statistical summaries into a functioning dashboard.

Group project: consists of three components completed in small groups. Students have the autonomy to select a problem of interest, source and analyse relevant data, apply appropriate statistical models, and develop an interactive dashboard to communicate insights to potential stakeholders through a written report and presentation with question and answer session delivered during a scheduled zoom session.

Reflective statement: The group project concludes with a reflection task, in which students are guided to reflect on aspects of the collaborative learning experience.

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 of a very high standard, a credit of a good standard, and a pass of an acceptable standard.

Result name	Mark range	Description
High Distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
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	Evaluate the effectiveness of dashboards in real-world decision-making environments	Workshop (1.5 hr)
Week 02	Dashboards – Tools and Design Principles	Workshop (1.5 hr)
Week 03	Shiny app development: UI and introduction to reactivity	Workshop (1.5 hr)
Week 04	Shiny app development: workflow, layout and dynamic UI	Workshop (1.5 hr)
Week 05	Shiny app development: mastering reactivity	Workshop (1.5 hr)
Week 06	Design in Action: Applied Dashboard Development	Workshop (1.5 hr)
Weekly	Self-directed learning before and after the workshop each week	Self-directed learning (24 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

Wickham, H. (2021). Mastering shiny. " O'Reilly Media, Inc.".

Healy, K. (2024). Data visualization: a practical introduction. Princeton University Press.

Villanueva, R. A. M., & Chen, Z. J. (2019). ggplot2: elegant graphics for data analysis.

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. Design and create interactive dashboards using industry-standards tools
LO2. Integrate both statistical thinking and computational skills to facilitate data-driven decision-making
LO3. Apply the principles of designing and creating effective interactive data visualisations
LO4. Analyse and select appropriate visualisations and narratives for diverse audiences
LO5. Evaluate the effectiveness of dashboards in real-world decison-making environments

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 is the first time this unit has been offered.

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

Disclaimer

On this page

Useful links

ODAT5032: Data to Decisions

PG Online Session 1B, 2026 [Online] - Online Program

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

Disclaimer

On this page

Useful links