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 2020 is available in the Archives.

UTS: Analytics and Data Science: Transdisciplinary Innovation
Credit points: 12 cp

Subject level:

Postgraduate

Result type: Grade, no marks

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

Requisite elaboration/waiver:

Any student wishing to enrol in first- and second-year subjects concurrently, needs to apply for a waiver.

Description

In this transdisciplinary innovation lab, students work individually and in teams to investigate traditional and emerging big data sets, and test theories or frameworks prior to rapidly developing a data driven prototype or proof of concept. In the iLab, they generate creative possibilities by combining new data sources with existing data. Each student and team is provided with a 'sandbox' to support them in designing experiments (for real or simulated stakeholders), evaluating the potential of different software technologies and developing key aspects of thinking like a data science professional. They consider the implications of their findings for different stakeholders and write a range of data narratives to explore the communication of data results for different purposes. Immersed in a lab environment oriented towards innovation and the execution of data driven experiments using real life, 'messy' data sets, students develop and study different workflows handling the extraction of value from diverse data types.

Subject learning objectives (SLOs)

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

1. Evaluate dynamic real-time data flows and identify the challenges of big and sparse data for understanding and acting on a system.
2. Contrast patterns and predictors for data discovery for the development of data science capabilities 
within organisations.
3. Explore concepts, frameworks and processes from other fields for their relevance to data science theory and practices in data driven experiments.
4. Construct a bricolage of problem solving approaches involving statistics and data formulations, visual explorations and machine learning techniques to discover deeper insights.
5. Deliver advice to stakeholders in the form of a multimodal narrative synthesis of the knowledge gained from data investigations, bridging the gap between data and human insight.
6. Apply relevant legislation and regulation and an understanding of stakeholders’ values to develop “privacy by 
design”.

7. 
Utilise understanding of team dynamics in complex 
organisational settings to design a team that can build and successfully deliver real-life and complex data driven projects.

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)
  • Making predictions and informing data discovery
    Analyse the value of different models, established assumptions and generalisations, about the behaviour of particular systems, for making predictions and informing data discovery investigations (1.3)
  • Making the invisible visible
    Use transdisciplinary approaches to seeing and doing to uncover underrepresented, or misrepresented, elements of a system (1.4)
  • Critiquing trends and theoretical frameworks
    Critique contemporary trends and theoretical frameworks in data science for relevance to one's own practice (2.1)
  • Exploring, interpreting and visualising data
    Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)
  • 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)
  • Designing and managing data investigations
    Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components (2.4)
  • 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)
  • Working together
    Develop a collaborative and team-oriented mindset to harness value for stakeholders to produce innovative solutions to challenges (3.3)
  • 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 you to immerse yourself in a lab environment to work with real-world challenges. You engage with stakeholders and negotiate projects that can create value through data-driven experiments. As part of this subject, you set your own learning goals and objectives and develop negotiated criteria for assessment.

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, a design laboratory brings together the theoretical and practical strands of data science innovation in this first capstone iLab 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.

Interactive workshops and masterclasses: Presentation and discussion formats involve introductions to innovative practice as well as critical debate and reflection of learning in context. Dynamic and interactive workshop sessions draw on speakers and trainers for workshops and master classes. These build upon in-class exercises and presentations to provide opportunities to develop innovative practice suited to data science contexts.

Online work: Canvas and other online media applications continue to be used to share information and encourage interaction between students, staff, stakeholders and experts drawn into the iLab program. Students post ongoing research and notes towards their project work, some of which they share with peers and staff for the purposes of feedback.

Design journal: Individual work in a design journal will provide opportunities for personal reflection and integration of the team's work on the emerging innovations. Regular feedback opportunities from peers and the iLab team are available in class sessions and online. 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)

  • Human-centred approaches to data value
  • Design-led innovation process and mindset
  • Innovation research tools and methods: Expert and practitioner sessions, students sharing and reflecting.
  • Working in the field.
  • Innovation Plan and Report: Working on the chosen challenge in combination of fieldwork, small group and individual tutorials.

Assessment

Assessment task 1: Project Design Journal

Objective(s):

1, 2, 3, 4, 5 and 6

Type: Project
Weight: 40%
Length:

14-20 pages/screens (approximately)

Assessment task 2: Project Showcase

Objective(s):

1, 2, 3, 5 and 7

Type: Presentation
Weight: 30%
Length:

15 minutes, with supporting documentation and data as required by the client brief

Assessment task 3: Professional Showcase

Objective(s):

1 and 7

Type: Portfolio
Weight: 30%
Length:

Negotiated with subject coordinator

Minimum requirements

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

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

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

https://research-data-network.readme.io/v1.03/docs/personal-data-resources

References

This is an indicative reference list. Specific texts students will be expected to read will be listed in Canvas and available as an electronic resource at UTS Library.

Baesens, B. 2014, Analytics in a big data world: the essential guide to data science and its applications, Wiley, Hoboken, NJ.

Brown, T., Katz, B. 2009. Change By Design: How Design Thinking Transforms Organisations And Inspires Innovation. New York: Harpercollins.

Bucolo, S., Wrigley, C. & Matthews, J. 2012, 'Gaps in organizational leadership: Linking strategic and operational activities through design-led propositions', Design Management Journal, vol. 7, no. 1, pp. 18-28.

Bucolo, S. & Wrigley, C. 2014, 'Design-led innovation: overcoming challenges to designing competitiveness to succeed in high cost environments' in Roos, G. & Kennedy, N. (eds), Global Perspectives on Achieving Success in High and Low Cost Operating Environments, IGI Global, pp. 241-251.

Christensen, C. M., Baumann, H., Ruggles, R., & Sadtler, T. M. 2006. Disruptive Innovation For Social Change. Harvard Business Review, 84(12).

Davenport, T.H. 2014, Big data at work: dispelling the myths, uncovering the opportunities, Harvard Business Review Press, Boston, MA.

Davila, T. & Epstein, M. 2014, The innovation paradox: why good businesses kill breakthroughs and how they can change, Berrett-Koehler, San Francisco, CA.

De Brabandere, L. & Iny, A. 2013, Thinking in new boxes: a new paradigm for business creativity, Random House, New York, NY.

Dobelli, R. 2013, The art of thinking clearly, Harper Collins, New York, NY.

Kahneman, D. 2011, Thinking, fast and slow, Farrar, Straus and Giroux, New York, NY

Manoochehri, M. 2013, Data just right: introduction to large-scale data & analytics, Pearson Education, Upper Saddle River, NJ

Provost, F. & Fawcett, T. 2013, Data science for business: what you need to know about data mining and data-analytic thinking, O’Reilly Media, Sebastopol, CA.

Schweitzer, J., Jakovich, J. 2012. Crowd-share Innovation - Intensive Creative Collaborations, Freerange Press, Sydney.

Tidd. J, Bessant, John., J. (2013), Managing Innovation, 5th Ed, John Wiley & Sons. Chichester, UK.

Watson, R. & Freeman, O. 2013, Futurevision: scenarios for the world in 2040, Scribe Publications, Brunswick, Vic, Australia.

Weinberger, D. 2014, Too big to know: rethinking knowledge now that the facts aren’t the facts, experts are everywhere, and the smartest person in the room is the room, Basic Books, New York, NY.

Other resources

Canvas and CIC Around will be used to distribute course material (including recommended readings), announcements and facilitate discussions