Unit outline_

MECO2604: Telling Stories with Data

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

In this unit students will explore theories and practices of information and data mediation within contemporary media and communications industries. Students will be introduced to key concepts and debates about the relationship between data and information, the uses and misuses of information, and the development of data journalism. From this theoretical base students will learn industry relevant practice in information verification and data storytelling using public databases and data visualisation tools.

Unit details and rules

Academic unit Media and Communications
Credit points 6
Prerequisites
? 
12 credit points at 1000 level in Media and Communications
Corequisites
? 
None
Prohibitions
? 
MECO3603
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Eugenia Lee, eugenia.lee@sydney.edu.au
The census date for this unit availability is 31 March 2026
Type Description Weight Due Length Use of AI
Out-of-class quiz Early Feedback Task Early Feedback Task
Quiz on unit content and assessments
0% Week 03
Due date: 13 Mar 2026 at 23:59
10 minute multiple choice quiz AI allowed
Outcomes assessed: LO1 LO3
Presentation group assignment Report Presentation
In-class group presentation
30% Week 04 8-10 min presentation equiv 1000 words AI allowed
Outcomes assessed: LO1 LO2 LO3
Data analysis Information Analysis
Critical analysis of relevant data sources based on a story topic
30% Week 07
Due date: 19 Apr 2026 at 23:59
1500 words AI allowed
Outcomes assessed: LO1 LO2 LO3
Data analysis Data Visualisation
Data story containing text and visualisation
40% Week 13
Due date: 31 May 2026 at 23:59
2000 words (equivalent) AI allowed
Outcomes assessed: LO2 LO3 LO4
group assignment = group assignment ?
early feedback task = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

All Assessments must be submitted in order to be eligible for a Pass or higher grade.

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

Work of an exceptional standard

Distinction

75 - 84

Work of a very high standard

Credit

65 - 74

Work of a good standard

Pass

50 - 64

Work of an acceptable standard

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. A detailed marking rubric is provided on the MECO2604 Canvas site.

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.

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

WK Topic Learning activity Learning outcomes
Week 01 What is data? Lecture (1 hr) LO1
Week 02 Mathematics in the humanities Lecture (1 hr) LO1 LO2 LO3
Mathematics in the humanities Tutorial (2 hr) LO1 LO2 LO3
Week 03 Generating ideas and finding data Lecture (1 hr) LO1 LO2 LO3
Generating ideas and finding data Tutorial (2 hr) LO1 LO2 LO3
Week 04 Data verification Lecture (1 hr) LO1 LO2 LO3
Data verification Tutorial (2 hr) LO1 LO2 LO3
Week 05 Manipulating data - scraping and cleaning Lecture (1 hr) LO1 LO2 LO3
Manipulating data - scraping and cleaning Tutorial (2 hr) LO1 LO2 LO3
Week 06 Data storytelling in practice Lecture (1 hr) LO1 LO2 LO3
Data storytelling in practice Tutorial (2 hr) LO1 LO2 LO3
Week 07 Software for hard data - introducing Flourish Lecture (1 hr) LO2 LO3 LO4
Software for hard data - introducing Flourish Tutorial (2 hr) LO2 LO3 LO4
Week 08 Visualising data Lecture (1 hr) LO1 LO2 LO3 LO4
Visualising data Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 09 Mapping data Lecture (1 hr) LO2 LO3 LO4
Mapping data Tutorial (2 hr) LO2 LO3 LO4
Week 10 Anatomy of a data story Lecture (1 hr) LO1 LO2 LO3
Anatomy of a data story Tutorial (2 hr) LO1 LO2 LO3
Week 11 Narrative in data storytelling Lecture (1 hr) LO1 LO2 LO3 LO4
Narrative in data storytelling Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 12 Data by design Lecture (1 hr) LO2 LO3 LO4
Data by design Tutorial (2 hr) LO2 LO3 LO4
Week 13 Data futures Lecture (1 hr) LO1 LO2 LO3 LO4
Data futures Tutorial (2 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

Attendance: Students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board, which will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.

Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. Please note you should not rely on lecture recordings to substitute your classroom learning experience.

Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

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

All readings can be accessed through the Reading List on the unit Canvas site.

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.

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

  • LO1. critically understand the history and theories of information and data mediation
  • LO2. use industry relevant methods and tools to identify and access data from public sources
  • LO3. apply critical reasoning to information verification and analysis
  • LO4. undertake data storytelling using techniques of data analysis and visualisation

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Minor updates to the reading list.

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.