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57193 Data and Computational Journalism

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2019 is available in the Archives.

UTS: Communication: Journalism
Credit points: 8 cp
Result type: Grade and marks

Requisite(s): 57083 Advanced Journalism
There are course requisites for this subject. See access conditions.


This subject prepares students to practice and engage with the reporting, analytical and academic challenges and opportunities presented by data and computational journalism. The subject introduces students to basic tools and techniques for data-driven reporting, enabling them to identify stories in datasets and use data to support their reporting. Students learn basic concepts of quantitative and statistical analysis and social science methodology, enabling them to critically assess datasets and the uses to which they are put. They practise techniques for collecting, verifying, cleaning and analysing data, with the aim of producing journalistic work incorporating information visualisations, and learn how to embed these data visualisations into online platforms. They engage with the emerging legal, ethical and philosophical debates surrounding 'big data', the open data movement and the public right to know.

Subject learning objectives (SLOs)

a. Locate, use, and critically assess data
b. Analyse data using basic tools and techniques of data-driven reporting
c. Evaluate techniques of data mining, cleaning and visualisation related to journalism
d. Apply basic techniques of quantitative and statistical analysis and social science method and their relevance to journalistic practice
e. Evaluate newsworthiness of data-driven stories
f. Explain how to contextualise data using a range of sources including human sources

Course intended learning outcomes (CILOs)

This subject engages with the following Course Intended Learning Outcomes (CILOs), which are tailored to the Graduate Attributes set for all graduates of the Faculty of Arts and Social Sciences:

  • Possess an advanced understanding of the professional skills and techniques in a range of contexts appropriate to contemporary journalism practice (1.1)
  • Apply a high level of initiative to create content using multiple techniques and within industry accepted frameworks of accountability (1.2)
  • Understand the complex capabilities of computer-assisted learning, data and other numeric-based techniques for advanced academic inquiry (2.1)
  • Reflect critically on the theory and professional practice of contemporary journalism (2.2)
  • Plan and execute a substantial research-based project, demonstrating advanced communication and technical research skills (2.3)

Teaching and learning strategies

This subject will be delivered in seminar mode, accompanied by extensive use of online, open resources for data-driven research skills, data driven reporting and data visualisation. Seminars will include discussion of current examples of data driven reporting which students will explore online in the course of the seminar. Seminars will be conducted in computer labs, in which students will develop and apply a range of data literacies. In-class work will be based on self-learning/practice-based modules that will be required before weekly classes. Students will receive

formative feedback on their use of basic tools and techniques of data-driven reporting. The subject incorporates a range of teaching and learning strategies including presentations from industry professionals, videos, exercises, project consultations and case studies.

Content (topics)

Topics to be covered include: the importance of data literacy and data-driven reporting for current journalism practice, how to design a data-driven investigation, tools and techniques of data-driven reporting, including advanced searching techniques, how to access hidden web resources such as databases and datasets, tools for scraping, cleaning and mining data mining, data verification, simple and advanced data visualization, basic statistical analysis and social science techniques, the ethics and sociology of “big data”, and the importance of emerging technologies to the future development of journalism and its role in democracy


Assessment task 1: Story Outline for Data Driven Investigation


a, b, e and f

Weight: 20%

750 words

Criteria linkages:
Criteria Weight (%) SLOs CILOs
Depth of analysis 25 b 2.2
Use of data research skills to develop an investigation 25 a 2.1
Newsworthiness of story brief 25 e 1.1
Range and depth of data and other sources included 25 f 1.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 2: Original Data Gathering Exercise


c, d and e

Weight: 30%

1000 words

Criteria linkages:
Criteria Weight (%) SLOs CILOs
Use of appropriate methods to collect and collate original and external data 25 d 2.1
Strength of story idea 25 e 1.1
Appropriateness of graphical and visual representations of data, which are described and analysed 25 c 2.1
Demonstration of key concepts in statistics and probability to draw conclusions from analysis 25 d 1.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 3: Data-Driven Journalistic investigation


a, b, c, d and e

Weight: 50%

1500 words or equivalent

Criteria linkages:
Criteria Weight (%) SLOs CILOs
Quality of data-focused research skills in development of an original story 15 a 2.3
Depth of analysis 15 b 1.1
Effectiveness of tools and techniques 15 c 2.1
Strength of evidence derived from data 20 d 2.2
Newsworthiness of story 20 e 1.1
Appropriateness of data visualisations to support the story 15 c 1.2
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Minimum requirements

Attendance at classes is essential in this subject because important information is only available through the essential workshopping and interchange of ideas with other students and the tutor, including collegial moderation of presentations and general assessment feedback. An attendance roll will be taken at each class. Students who have more than two absences from class will be refused the marking of their final assessment (see Rule 3.8).

Required texts

There are no required texts for this subject. Recommended readings and resources will be available via UTS Library and Canvas.


Cushion, S., Lewis, J. & Callaghan, R. 2017, 'Data journalism, impartiality and statistical claims: Towards more independent scrutiny in news reporting', Journalism Practice, vol. 11, no. 10, pp. 1198-215.

Henninger, M. 2008, The hidden web: finding quality information on the net, 2nd edn, UNSW Press, Sydney NSW

Henninger, M. 2013, 'Information sources and data discovery', in A. Knight (ed.), Challenge and change: Reassessing journalism's global future, UTS ePress, Sydney, Australia, pp. 185-215.

Houston, B. 2009, The investigative reporter's handbook : a guide to documents, databases and techniques, 5th edn, Bedford/St. Martin's, Boston.

Loosen, W., Reimer, J. & De Silva-Schmidt, F. 2017, 'Data-driven reporting: An on-going (r) evolution? An analysis of projects nominated for the Data Journalism Awards 2013–2016', Journalism. First Published October 2017

McGhee, G. 2009-2010, Journalism in the age of data: A video report on data visualization as a storytelling medium [video], available at

Tufte, E.R. 2001, The visual display of quantitative information, 2nd edn, Graphics Press, Cheshire, Conn.

Walkenbach, J. 2013, 101 Excel 2013 Tips, Tricks and Timesavers, John Wiley & Sons, Hoboken, NJ.

Walkenbach, J., 2015, Microsoft® Excel® 2016 Bible [ebook], Wiley, Indianapolis, Ind.

Young, M.L., Hermida, A. & Fulda, J. 2018, 'What Makes for Great Data Journalism? A content analysis of data journalism awards finalists 2012–2015', Journalism Practice, vol. 12, no. 1, pp. 115-35.

Many excellent data journalism resources are available online, for example

The Curious Journalist’s Guide to Data available at

Datajournalism Handbook, available at