University of Technology Sydney

36100 Data Science for Innovation

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: Analytics and Data Science: Transdisciplinary Innovation
Credit points: 8 cp

Subject level:


Result type: Grade, no marks

Requisite elaboration/waiver:

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


This subject introduces students to the cutting-edge initiatives that organisations and professionals are embarking on to extract and communicate value from data. The subject contextualises professional data science practices for innovation by drawing together skills and knowledge regarding data, communication, and ethics.

Students examine contemporary cases illustrating how novel data sources can act as catalysts to drive innovation and transform industries and professions globally in health, finance, insurance, management, marketing, journalism, librarianship, education, science, transportation, aviation and the environment. First-hand exploratory interaction with data enables students to engage in inquiry-based research to seek informative correlations, patterns and trends leading to discoveries, and identify data discovery studies of interest to them. Through hands-on data discovery experiments, students explore data as complex and heterogeneous, with new techniques and technologies raising implications for approaches to the ethical and legal collection of personal data.

Subject learning objectives (SLOs)

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

1. Determine opportunities to solve problems using data within disciplinary practices.
2. Analyse current trends and professional practices in data science innovation.
3. Extract value from structured and unstructured data.
4. Communicate data discovery projects in a manner appropriate for the discipline, audience and purpose.
5. Evaluate privacy, ethical and legal implications of collecting and publishing personal data.

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)
  • Critiquing trends and theoretical frameworks
    Critique contemporary trends and theoretical frameworks in data science for relevance to one's own practice (2.1)
  • 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)
  • 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 as a student to understand relationships and processes within systems as well as to apply theoretical frameworks to examine contemporary real-world cases. Through hands-on data investigations you identify the potential for data to provide insights that can inform strategic innovation. The subject also focuses on developing your communication skills to engage audiences and inform decision-making. Finally, the subject invites you to explore your ethical responsibilities in data-rich professional contexts and begin developing your skills as a reflective data practitioner.

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

Teaching and learning strategies

Blend of online and face to face activities: This subject is offered through a series of block sessions and blends online with face-to-face learning. Students participate in interactive learning experiences in timetabled on-campus sessions, where they make use of the subject materials that they have already engaged with online. In between campus sessions, students will engage in individual and collaborative online activities designed to consider a range of challenges associated with big data and innovation.

Transdisciplinary approaches: Starting from an elemental perspective on data and data science, students will approach learning from their specific professional and potential future contexts. As the subject progresses conceptual and philosophical approaches to the association between data and innovation, provocative questions will stimulate analytical engagement across a range of perspectives. Case studies and insights from industry experts will provide 'lived experiences' to accelerate and consolidate student learning.

Future-oriented strategies: Students will be exposed to contemporary learning models utilising disruptive, controversial and speculative thinking, as well as reflection. Electronic portfolios will be utilised to curate, consolidate and provide evidence of learning and development of course outcomes, graduate attributes and professional evolution. Formative feedback and suggestions for successful engagement with all assessment activities will be offered throughout the session.

Collaborative work: A strong emphasis is placed on group activities and interaction, given that graduates of this course will need to approach professional projects and challenges from a collaborative and consensus position. Individual insight is shared and reworked in groups analysing data sets for trends and assumptions. Group activities will enable students to leverage peer-learning and demonstrate effective team participation and contribution, as well as an appreciation of diverse perspectives on data science and innovation.

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

Content (topics)

The subject covers a set of topics focused around three core assessments, weekly material will explore:

  1. Identifying data science innovation and opportunities for innovation using data science, including challenges and ethical issues in this this area
  2. The human face of data and the collection and collation of that data
  3. Meaningful representation of data and talking about personal data
  4. Applying ethical, privacy, and legal issues to data science


Assessment task 1: Analysis of Contemporary trends in data science


Students write a professional discussion paper, drawing on their analysis of the potential opportunities for data driven innovation within a selected professional context.


1, 2, 4 and 5

Type: Essay
Groupwork: Individual
Weight: 25%

1800 words

  • Effectiveness in evaluating the key factors or challenges responsible for driving innovation in the organisation or sector
  • Clarity in articulating opportunities for the organisation to make innovative use of data to address challenges.
  • Insightfulness in evaluating issues in the data transformation process (e.g., obtaining, cleaning, validating, storing, , keeping secure & maintaining privacy of, transforming, and analysing data) within the organisation.
  • Level of professionalism in the presentation as appropriate to the discipline.

Assessment task 2: Quantified Self Project: stories and accounts discovered in data relationships


Students engage in exploration of personal data and the production of a compelling data-narrative through investigating relationships in data, and the potential for data to provide (innovation) insight into one's own life, and the implications of that for policy debates regarding data privacy and life.


2, 3, 4 and 5

Type: Project
Groupwork: Individual
Weight: 75%

Part A: Less than 1 page (submitted via a google form);

Part B: A substantive draft should be submitted for feedback, and peer feedback must be provided.

Part C: 3000 words (excluding data excerpts, visualisations, and references)

  • Strength of justification for the method to obtain data from multiple sources, for gaining insight into a chosen problem, including analysis of data quality issues in the individual and group data
  • Insightfulness in the analysis of the obtained data, including quality issues, to draw conclusions in a professional and engaging manner
  • Insightfulness in identification, contextualisation and reflection on ethical, privacy, and legal issues relevant to the collection, analysis, and use of one's own and other's personal data
  • Strength of connection between the individual experience of this QS project to the practice of data science (and the preceding three criteria)
  • Level of professionalism in the presentation appropriate to the discipline

Minimum requirements

Students must participate in all online and face to face requirements, as well as achieving a Pass in all assessment tasks.

In the case of failing marks students may by given the opportunity to submit for a maximum of a Pass mark.

Recommended texts

Students may find one of the following introductory texts useful:

O’Reilly Media (Ed.). (2014). Big Data Now (2014 edition). O’Reilly Media.

Burlingame, N. & Nielsen, L. 2012, A simple introduction to data science, New Street Communications, Wickford, Rhode Island.

Sangameswar, S. 2014, Big data: an introduction, CreateSpace Independent Publishing Platform.


Other Useful Sources:

Note: The below provides a list of contemporary sources on the topic of data science, many of which provide accessible book length introductions to particular concerns in the space. There is no expectation that students will read all (or, indeed, any) of these sources, however many of them will be of interest to you, and you may wish to peruse the list if you are looking for some additional introductions.

Brynjolfsson, E. & McAfee, A. 2014, The second machine age: work, progress, and prosperity in a time of brilliant technologies, Norton & Company, London, UK.

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

Davenport, T.H., Harris, J.G. & Morison, R. 2010, Analytics at work: smarter decisions, better results, Harvard Business Review Press, Harvard, Massachusetts.

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

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

Eggers, D. 2013, The circle, Random House, New York City, NY.

Evergreen, S.D.H. 2013, Presenting data effectively: communicating your findings for maximum input, Sage, Thousand Oaks, California.

Feinleib, D. 2013, Big data demystified: how big data is changing the way we live, love and learn, The Big Data Group.

Foreman, J.W. 2013 Data smart: using data science to transform information into insight, Wiley, Indianapolis, Indiana.

Fung, K. 2013, Numbersense: how to use big data to your advantage, McGraw-Hill.

Gemignani, Z., Gemignani, C., Galentino, R. & Schuermann, P. 2014, Data fluency: empowering your organization with effective data communication, Wiley, Indianapolis, Indiana.

Gigerenzer, G. 2014, Risk savvy: how to make good decisions, Viking.

Gitelman, L. (Ed.) 2013,“Raw data” is an oxymoron, Massachusetts Institute of Technology, Boston, Massachusetts.

Gutierrez, S. 2014, Data scientists at work, Apress.

Hayes, N.K. 2012, How we think: digital media and contemporary technogenesis, University of Chicago Press, Chicago, Illinois.

Isaacson, W. 2014, The innovators: how a group of hackers, geniuses, and geeks created the digital revolution, Simon & Schuster, New York City, NY.

Kahneman, D. 2013, Thinking, fast and slow, Farrar, Straus and Giroux, New York City, NY.

Kellmereit, D. & Obodovski, D. 2013, The silent intelligence: the internet of things, DND Ventures.

Kolb, J. & Kolb, J. 2013, The big data revolution, Applied Data Labs Inc.

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

Marr, B. 2015, Big Data: using SMART big data, analytics and metrics to make better decisions and improve performance, Wiley & Sons, West Sussex, UK.

Marz, N. & Warren, J. 2015, Big data: principles and best practices of scalable real-time data systems, Manning, Shelter Island, New York.

Mayer-Schonberger, V. & Cukier, K. 2013, Big data: a revolution that will transform how we live, work, and think, John Murray, London, UK.

Merks-Benjaminsen, J. 2015, Think and grow digital: what the net generation needs to know to survive and thrive in any organization, McGraw-Hill.

Moore, D.T. 2013, Sensemaking: a structure for an intelligence revolution (2nd edn), CreateSpace Independent Publishing Platform.

Morieux, Y. & Tollman, P. 2014, Six simple rules: how to manage complexity without getting complicated, Harvard Business Review Press, Boston, Massachusetts.

Ojeda, T., Murphy, S.P., Bengfort, B. & Dasgupta, A. 2014, Practical data science cookbook: real-world data science projects to help you get your hands on your data , Packt Publishing, Birmingham, UK.

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, California.

Sathi, A. 2013, Big data analytics: disruptive technologies for changing the game, MC Press Online, Boise, Idaho.

Schmidt, E. & Rosenberg, J. 2014, How Google works, Grand Central, New York City, NY.

Schneier, B. 2015, Data and Goliath: the hidden battles to collect your data and control your world, W. W. Norton & Company.

Scoble, R. & Israel, S. 2013, Age of context: mobile, sensors, data and the future of privacy, Patrick Brewster Press.

Shirky, C. 2009, Here comes everybody: the power of organizing without organizations, Penguin, New York City, NY.

Shirky, C. 2010, Cognitive surplus: creativity and generosity in a connected age, Penguin, New York City, NY.

Voulgaris, Z. 2014, Data scientist: The definitive guide to becoming a data scientist, Technics, Basking Ridge, New Jersey.

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 City, NY.


There are a plethora of podcast and video series on data science, these include (but are not limited to):

IBM Big Data & Analytics Hub Podcasts:

BBC Radio 4: The Bottom Line Podcasts:

  • Big data (2013)
  • Wearable technology (2014)

McKinsey on High Tech Podcasts:

  • Big data
  • The Internet of Things

TED Talks
Cukier, K. Big data is better data
Etlinger, S. What do we do with all this big data?
Thorp, J. Make data more human
McCandless, D. The beauty of data visualization
Wellington, B. How we found the worst place to park in New York City - using big data
Collection - Making sense of too much data (13 talks)
Collection - The dark side of data (14 talks)
Collection - Art made of data (5 talks)

Other resources

Canvas will be used to distribute course materials (including recommended readings) and announcements.