University of Technology Sydney

36105 iLab 2

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 AND 36104 Data Visualisation and Narratives AND 36102 iLab 1
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.

Description

This second innovation lab subject focuses students towards designing, realising and investigating a prototype in which they utilise contemporary techniques and large, complex, multi-structure data sets. Students develop project documentation and supporting artefacts in the lab environment suitable for adaption in a work experience context. They test new approaches from current research literature, or propose new studies, under the supervision of transdisciplinary staff. The lab environment fosters collaboration where students working individually and in groups openly share data and results of data explorations. 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. 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. The work experience project extends the lab activities to provide students with an opportunity to pursue professional interests, whether involving not-for-profit communities, an entrepreneurial proposal or a nominated organisation. Students communicate the outcomes of their innovation lab and workplace investigations in various forms to relevant audiences.

Subject learning objectives (SLOs)

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

1. Identify the unknown patterns in big data sets and opportunities for analytic innovation by combining new sources of data and different analytic models
2. Integrate new and open data sources with transdisciplinary research to enrich in-house data sense making, inform trends and propose new products or services

3. Create value for industry clients 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 critically assessing data sets and developing cogent and convincing multi-modal data narratives and client reports
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. Take a leadership role in industry teams, defining and executing solutions to current data challenges, balancing specific stakeholder needs and values with organisational priorities
7. Identify the changes necessary to create a future-focused, data driven organisation and apply human centered practices to build organisational capacity and innovation
8. Build upon their transdisciplinary knowledge and skills to be an ‘agent of change’ in the data science profession. 


Course intended learning outcomes (CILOs)

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

  • 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)
  • Examining and articulating data value
    Critically examine the perceived value of data analytics outcomes and clearly articulate implications for different stakeholders and organisations (3.2)
  • 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)
  • Leading data science
    Take a leadership role in promoting positive change in data science contexts, recognising individual, organisational and community issues (5.3)

Contribution to the development of graduate attributes

The subject creates and opportunity for you to pursue your professional interests and carry out a real-world project in collaboration with stakeholders and the cohort. You apply a range of research-informed approaches to generate discoveries that are valuable in a given problem space. 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 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

Interactive master classes and clinics: Master classes are facilitated by industry experts and specifically tailored to
students’ needs to inform and extend their approaches to innovative projects in data science practice. The clinics are
facilitated by the CIC team and provide exploratory spaces to further investigate these project approaches, gain
feedback on project progress and discuss challenges with professionals and peers. Collaborative critical evaluation of
contemporary industry case studies will enable students to extrapolate implications for successful projects and their
future practice. Critical feedback, debate and 'reflection-on-action' will support student decision-making and project
progress.
Students will have the opportunity to crystallise key skills and capabilities gained while undertaking the core and
elective subjects already undertaken in their MDSI course. It is expected that they will be able to draw on prior
learning, and engage in an explicit and ongoing process of individually and collaboratively evaluating strengths, ‘blind
spots’ and areas that need to be addressed and this will underpin on campus and online work across this subject.
Students will be mentored to build on and assimilate their knowledge and skills as professionals and support their
transition into future leaders in data science.

Capstone Project: Each student proposes, develops and executes a capstone project, which consolidates students’
experience and learning in data science and innovation over the degree program. Projects are negotiated with the
subject coordinator and mentors (see below).

Professional and Academic Mentors: Each student will be allocated both an academic and an industry mentor.
They will provide ongoing feedback on project progress and strategies for responding to emerging challenges.

Professional industry collaboration: Building on the first iLab subject students collaborate with industry teams using
an emergent approach to solving data science challenges. Students will work collaboratively (in the interactive master
classes and clinics and the online community) with their professional and academic mentors to translate their
knowledge and skills into practice and contexts and produce innovative solutions for organisational or community
challenges.

Online iLab Community: Online channels will be used to share information between students, staff, stakeholders and
industry experts participating in the iLab program. Students will post weekly project updates based on their project
portfolios and receive feedback from the iLab community.

Weekly study and preparation activities, as well as detailed assessment information are provided in Canvas.

Content (topics)

  • 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 in dynamic industry teams
  • Innovation research for specific challenges and projects
  • Achieving robust and unique outcomes to current and future data challenges.

Assessment

Assessment task 1: Project Workbook

Objective(s):

1, 2, 3, 4, 5, 6, 7 and 8

Type: Portfolio
Groupwork: Individual
Weight: 35%
Length:

Weekly summaries 150-250 words or equivalent; Interim Summary 750-1000 words; Final selection 1500 words.

Assessment task 2: Client Status Report and Briefing

Objective(s):

1, 2, 3, 4, 5, 6, 7 and 8

Type: Presentation
Groupwork: Group, individually assessed
Weight: 20%
Length:

Presentation 10 minutes. Briefing report 500 words equivalent.

Assessment task 3: Project Synthesis and Evaluation

Objective(s):

1, 2, 3, 4, 5, 6, 7 and 8

Type: Project
Groupwork: Individual
Weight: 45%
Length:

3000 words equivalent maximum, with supporting documentation as required by the client & project context.

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

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 http://bdes.datasociety.net/council-output/perspectives-on-big-data-ethics-and-society/.

OECD (2015). Data-Driven Innovation: Big Data for Growth and Well-Being, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/9789264229358-en

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.

Other resources

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