University of Technology Sydney

94676 Technology Lab 2: Connect and Network

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 2021 is available in the Archives.

UTS: Transdisciplinary Innovation
Credit points: 8 cp
Result type: Grade and marks

Requisite(s): 94674 Technology Lab 1: Imagine and Create


In this subject students focus on connecting and networking ideas, data, technologies, people, organisations and practices. Tools for collecting, processing, modelling, representing and visualising data in various forms are explored and used to better understand complex problems. First-hand experiences of working with data in networked environments, as well as experimenting with technologies, challenge students to collaborate and pursue data discovery studies of high interest to them. The lab supports teams to connect multiple data sources and tools together to create models and design prototypes, and to communicate technological ideas within and beyond the team.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Experiment connecting ideas, practices, data sources, and/or people/organisations with technologies
2. Work with data sets to represent and interrogate them using various technologies
3. Design networked prototypes of technological solutions to data-driven systems
4. Investigate and interpret technological solutions for complex data-driven systems

Course intended learning outcomes (CILOs)

This subject contributes specifically to the development of the following course intended learning outcomes:

  • Critically examine technologies to enhance doing things in social and professional contexts (1.1)
  • Use script, digital tools, techniques and practices to build applications and devices (1.2)
  • Apply abstraction and test proposed design for a digital application (1.4)
  • Select, apply and evaluate various techniques and technologies for investigating and interpreting complex systems (4.2)

Contribution to the development of graduate attributes

This subject provides opportunities for you as a student to critically examine technologies and use script, digital tools and techniques to build applications and devices that have the potential to enhance practices in social and professional contexts. You experiment and test your proposed designs and select, apply and evaluate various techniques to investigate and interpret complex systems.

So your experiences as a student in this subject support you to develop the following graduate attributes (GA):

  • GA 1 - Technological fluency and computational thinking
  • GA 4 - Resilient practices within complex systems

Teaching and learning strategies

This subject consists of a 4 hour class per week, supplemented by online modules and activities. Students are given preparatory work before every lesson, posted on Canvas, where they are asked to engage and reflect on material that include critical and instructional video and text. This preparatory work includes pre-reading, watching presentations and technical tutorials or undertaking short technical classes on sites like Lynda or Data Camp. In class, students and teachers work together on exercises and discussion based on this preparatory work. This allows students to dig deeper into the material, and collaborate and engage with their teachers and peers on the weekly topics. Real-time feedback is given to students during the weekly classes, along with a reflective learning exercise early and at the end of the semester.

Content (topics)

  • Abstraction and modelling of systems
  • Methods for acquiring data
  • Data wrangling, massaging and interpretation (from multiple perspectives)
  • Communicating data stories (visualisations and narratives) for data supported decision making
  • Connecting technological tools


Assessment task 1: Data discovery


This task addresses the following subject learning objectives:

1 and 2

This assessment task contributes to the development of course intended learning outcome(s):

1.1 and 1.2

Type: Report
Groupwork: Individual
Weight: 30%

Assessment task 2: Data connections


This task addresses the following subject learning objectives:

1, 2 and 4

This assessment task contributes to the development of course intended learning outcome(s):

1.1, 1.2 and 4.2

Type: Project
Groupwork: Group, group assessed
Weight: 30%

Assessment task 3: Data visualisation and interpretation


This task addresses the following subject learning objectives:

3 and 4

This assessment task contributes to the development of course intended learning outcome(s):

1.4 and 4.2

Type: Case study
Groupwork: Group, individually assessed
Weight: 40%

Minimum requirements

Students must attempt each assessment task and achieve an overall pass mark in order to pass this subject.

Students are required to attend 80% of all scheduled classes in this subject. This normally means you can miss at most two (2) weekly classes. If you do not meet this requirement, you may be refused permission by the Responsible Academic Officer to be considered for assessment in this subject (UTS rule 3.8.2), which may result in you failing the subject. If the reason for your absence is due to illness or misadventure, please follow the normal procedures to apply for special consideration.

Late penalties apply to all assessment tasks as outlined in the FTDi FYI student booklet. Please consult this booklet for other useful information including Special Consideration, Plagiarism, Extension, and Student Support Services.

Recommended texts

Bertin, J. 1967, Semiology Of Graphics: diagrams networks maps, trans. W.J. Berg, 3rd edn, Esri Press, Redlands, California.

Cairo, A. 2013, The functional art?: an introduction to information graphics and visualization, electronic., New Riders, Berkeley, California.

Dörk, M., Carpendale, S. & Williamson, C. 2011, 'The information flaneur: A fresh look at information seeking', Proceedings of the 29th SIGCHI Conference on Human Factors in Computing Systems, pp. 1215–24.

Kirk, A. 2016, Data Visualisation: A Handbook for Data Driven Design, 1st Editio., SAGE Publications, London, UK.

Lima, M. 2014, The Book of Trees: Visualizing Branches of Knowledge, New York, NY.

Lima, M. 2011, Visual Complexity: mapping patterns of information, 1st edn, Princeton Architectual Press, New York, NY.

Lupi, G. 2015, 'The New Aesthetic to Data Narrative', in D. Bihanic (ed.),New Challenges for Data Design, Springer, London.

Lupi, G. & Posavec, S. 2016, Dear Data, Penguin Press, London, UK.

Shneiderman, B. 1996, 'The eyes have it: a task by data type taxonomy for information\nvisualizations', Proceedings 1996 IEEE Symposium on Visual Languages, pp. 336–43.

Thorp, J. 2013, 'Vizuasation is a Process, not an output', Harvard Business Review, Harvard Business Review, viewed <>.

Tufte, E.R. 2001, The Visual Display of Quantitative Information, 2nd edn, Graphics Press, Cheshire, Conneticut.

Wilkinson, L. 2005, The Grammar of Graphics, Springer-Verlag, New York, NY, USA.