University of Technology Sydney

36102 iLab 1

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 2023 is available in the Archives.

UTS: Analytics and Data Science: TD School
Credit points: 12 cp

Subject level:


Result type: Grade, no marks

Requisite(s): 36100 Data Science for Innovation AND 36103 Statistical Thinking for Data Science AND 36106 Machine Learning Algorithms and Applications
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.


The innovation lab subject focuses students towards designing, realising and investigating a data-driven prototype in which they utilise contemporary techniques and multi-structure data sets. Student teams work on diverse data-driven solutions for complex real-world challenges presented by industry and academic partners. They develop technical outputs, project documentation and supporting artefacts in the lab environment suitable for adaption in a work context. They test new approaches from current research literature and industry standards, under the supervision of transdisciplinary staff in structured project phases and share the results of data explorations. Using a range of open data sets and suitable case studies, students hone their skills in framing questions useful for embarking upon discoveries, addressing gaps in knowledge or tackling problems. Students communicate the outcomes of their innovation lab investigations in various forms to relevant audiences. Through taking responsibility for an aspect of a workflow to extract value, or alternatively, work on a subset or a thematic area of inquiry, each student also contributes to the work stream of a major project.

Subject learning objectives (SLOs)

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

1. Respond to complex challenges by innovating with new sources of data and different analytic techniques
2. Find and integrate open data sources to enrich in-house data sense making, inform trends and propose new products or services
3. Create value for partners through problem finding and problem solving in organisations and applying human centered approaches to data science investigations
4. Effectively communicate a project’s vision, execution, value and outcomes to a range of stakeholders through cogent multi-modal data narratives
5. Respond to complex data challenges as ethical data professionals by managing data science projects and documenting practices to enable appropriate implementation of relevant policy and governance measures
6. Build effective team dynamics and professionalism by managing real-life data science projects
7. Build upon their transdisciplinary knowledge and skills to be an ‘agent of change’ in the data science profession
8. Design and implement a self-directed and reflective learning process in a professional practice context

Course intended learning outcomes (CILOs)

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

  • Understanding relationships & processes within systems
    Identify and represent the human and technical elements and processes within complex systems and organise them within frameworks of relationships (1.1)
  • Exploring and testing models and describing behaviours of complex systems
    Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)
  • Understanding uncertainty, ambiguity and complexity
    Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data (2.3)
  • Developing strategies for innovation
    Explore, interrogate, generate, apply, test and evaluate problem-solving strategies to extract economic, business, social, strategic or other value from data (3.1)
  • Developing communication skills
    Collaborate to develop and refine multimodal communication skills needed to successfully work in data science teams (4.1)
  • Engaging audiences
    Explore and craft interpretative narratives that engage key audiences with data analytics and potential significance for action, at a societal, industrial, organisational, group or individual levels (4.2)
  • Informing decision making
    Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences (4.3)
  • Becoming a reflective data practitioner
    Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts (5.1)
  • Embracing ethical responsibilities
    Interrogate and justify ethical responsibilities related to data selection, access, analysis and governance to create a framework for practice (5.2)

Contribution to the development of graduate attributes

The subject provides opportunities for students to immerse themselves in a lab environment to work with real-world challenges. They apply transdisciplinary thinking to come up with new ideas and recommend insights for innovation opportunities in partner organisations. Students engage with team members and derive data driven solutions that can add value for partners.

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

  • GA 1 - Sociotechnical systems thinking
  • GA 2 - Creative, analytical and rigorous sense making
  • GA 3 - Create value in problem solving and inquiry
  • GA 4 - Persuasive and robust communication
  • GA 5 - Ethical citizenship and leadership

Teaching and learning strategies

Transdisciplinary approach: Through a dynamic immersion into the world of designing creative, value-driven solutions, this subject brings together the theoretical and practical strands of data science innovation in this subject. An evolving, emergent approach to data science challenges is implemented in team projects students choose to tackle over the session. Students work as innovators looking to add value to particular organisational or community challenges to produce working prototypes that could be the seed for real organisational innovation or a start-up enterprise at the end of session.

Team collaborations: students collaborate in design teams and utilise diverse perspectives to innovate and solve problems in data science and innovation. Disruptive, controversial and speculative thinking, as well as reflection are built upon to encourage critical approaches to working in teams.

Online work: Canvas and other online media applications continue to be used to share information and encourage interaction between students, staff and stakeholders in the iLab program.

Design journal: Individual work in a design journal for tracking progress will provide opportunities for personal reflection and integration of the team's work on the emerging innovations. Portfolio thinking continues to frame students' curation, consolidation and communication of evidence of their learning and development of graduate attributes and professional evolution.

Content (topics)

  • Application of key knowledge, skills and capabilities gained in the MDSI course to date
  • Human-centred data innovation in the field
  • Design-led innovation processes and mindsets
  • Innovation research for specific challenges and projects
  • Achieving robust and unique outcomes to current and future data challenges.
  • Working with real-world data


Assessment task 1: Progress journal and reflection


Part A: Group Progress tracker for project goals (AT1A)

Part B: Progress log and reflection (AT1B)


1, 4 and 5

Type: Portfolio
Groupwork: Group, individually assessed
Weight: 30%

Assessment task 2: Project outcomes

Intent: Final project report and individual contributions (AT2)

2, 3, 4 and 7

Type: Report
Groupwork: Group, group and individually assessed
Weight: 40%

Assessment task 3: Presentation and evaluation


Part A: Interim Presentation (AT3A); Part B: Partner and Peer evaluation (AT3B)


2, 3, 4 and 5

Type: Presentation
Groupwork: Group, individually assessed
Weight: 30%

Minimum requirements

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

Late penalties apply as outlined in theUTS student booklet. Please consult this booklet for other useful information including Special Consideration, Plagiarism, Extension, and Student Support Services.

Recommended texts

For a round up of resources for managing & working with personal data – standards, best practice, anonymisation issues, please visit:


Ashwin, K. T., Kammarpally, P., & George, K. M. (2016, January). Veracity of information in twitter data: A case study. In 2016 International Conference on Big Data and Smart Computing (BigComp) (pp. 129-136). IEEE.

Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., & Buyya, R. (2015). Big Data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing, 79, 3-15

Baker, P., & Gourley, B. (2014). Data Divination: Big Data Strategies. Delmar Learning.

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.

Blasius, J., & Greenacre, M. (Eds.). (2014). Visualization and verbalization of data. CRC Press.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press.

Davis, K. (2012). Ethics of Big Data: Balancing Risk and Innovation. O'Reilly Media.

Debattista, J., Lange, C., & Scerri, S. (2015, December). Linked'Big'Data: Towards a Manifold Increase in Big Data Value and Veracity. In 2015 IEEE/ACM 2nd International Symposium on Big Data Computing (BDC) (pp. 92-98). IEEE.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35, 137-144.

Howard, A. (2015). Humanise: Why Human-Centred Leadership is the Key to the 21st Century. John Wiley & Sons.

Hu, R. (2015). Sustainability and competitiveness in Australian cities. Sustainability, 7(2), 1840-1860

Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.

Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google flu: traps in big data analysis. Science, 343(6176), 1203-1205.

Lee, M. R. (2013). Leading virtual project teams: Adapting leadership theories and communications techniques to 21st century organizations. CRC Press.

Lepsinger, R., & Lucia, A. D. (2009). The art and science of 360 degree feedback. John Wiley & Sons.

Li, J., Tao, F., Cheng, Y., & Zhao, L. (2015). Big Data in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 81(1-4), 667-684.

Li, S, & Gao, J. Security and Privacy for Big Data. In Big Data Concepts, Theories, and Applications, pp. 281-313. Springer International Publishing, 2016

Matei, S. A. & Collman, J. (eds.) (2016) Ethical Reasoning in Big Data: An Exploratory Analysis. Springer.

Metcalf, J., Keller, E.F. & boyd, d. (2016). Perspectives on Big Data, Ethics, and Society. Council for Big Data, Ethics, and Society. Available

OECD (2015). Data-Driven Innovation: Big Data for Growth and Well-Being, OECD Publishing, Paris. DOI:

Sänger, J., Richthammer, C., Hassan, S., & Pernul, G. (2014, September). Trust and big data: a roadmap for research. In 2014 25th International Workshop on Database and Expert Systems Applications (pp. 278-282). IEEE.

Schön, D. A. (1995). The reflective practitioner: How professionals think in action. Aldershot, England : Arena, (1995 Edition)

Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc.

Silver, N. (2012). The signal and the noise: Why so many predictions fail-but some don't. Penguin.

Tien, J. M. (2013). Big data: Unleashing information. Journal of Systems Science and Systems Engineering, 22(2), 127-151

Yu, S., & Guo, S. (2016). Big Data Concepts, Theories, and Applications. Springer.