36100 Data Science for Innovation
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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 2024 is available in the Archives.
Credit points: 8 cp
Subject level:
Postgraduate
Result type: Grade, no marksRequisite elaboration/waiver:
Any student wishing to enrol in first- and second-year subjects concurrently, needs to apply for a waiver.
Description
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. | Conduct classification and regression tasks involves the creation of models capable of categorizing data points into predetermined classes or predict a continuous numerical outcome. Modelling |
| 5. | Communicate data discovery projects in a manner appropriate for the discipline, audience and purpose. |
| 6. | 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) - 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) - 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) - 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.
An aim of this subject is to help you develop academic and professional language and communication skills in order to succeed at university and in the workplace. To determine your current academic language proficiency, one of the assessment tasks in this subject will be used to assess your level of academic English language. If you receive an unsatisfactory level for English language, you must attend follow up language development activities, in order to pass the subject. These activities are designed to support you to develop your language and communication skills. Students who do not attend 80% of the language development activities will receive a Fail X grade for the subject.
Content (topics)
The subject covers a set of topics focused around three core assessments, weekly material will explore:
- Identifying data science innovation and opportunities for innovation using data science, including challenges and ethical issues in this this area
- The human face of data and the collection and collation of that data
- Meaningful representation of data and talking about personal data
- Applying ethical, privacy, and legal issues to data science
Assessment
Assessment task 1: Lab exercises
| Intent: | Students complete and submit the weekly exercises to ensure continuous learning and engagement with the course material. |
|---|---|
| Objective(s): | 1, 2, 5 and 6 |
| Type: | Laboratory/practical |
| Groupwork: | Individual |
| Weight: | 10% |
| Length: | Not defined |
| Criteria: |
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Assessment task 2: Quantified Self Project: stories and accounts discovered in data relationships
| Intent: | 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. |
|---|---|
| Objective(s): | 2, 3, 5 and 6 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 60% |
| Length: | stage 1: Report ~2000 words (excluding appendix, visualisations, and references) stage 2: Report ~3000 words (excluding appendix, visualisations, and references) stage 3: Video presentation ~2-3 mins per member |
| Criteria: |
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Assessment task 3: Online Quiz
| Intent: | Students complete two online quizzes to evaluate their understanding of all teaching materials. |
|---|---|
| Objective(s): | 1, 2 and 3 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | One hour quiz |
| Criteria: |
|
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.
As a University policy, it is a requirement of this subject that all students complete a language screening task (as part of one of your assessments in this subject). Students who received an unsatisfactory level for written English language are required to attend 80% of the language development activities in order to pass the subject. Students who do not attend 80% of the language development activities will receive a Fail X grade.
Recommended texts
Recommended texts are referred to throughout the subject materials.
Students may find it useful to refer to more general introductions to data science, or/and to specific examples of applying data science within their own discilpinary or professional context.
References
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.
We would encourage you to share resources you find useful and interesting - whether from the course materials, or ones you have found yourselves - on the subject platform.
Book length introduction texts
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.
Book length texts on how data science is being used in business innovation
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.
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.
Isaacson, W. 2014, The innovators: how a group of hackers, geniuses, and geeks created the digital revolution, Simon & Schuster, New York City, NY. - an overview of the history of computer science and the Digital Revolution
Scoble, R. & Israel, S. 2013, Age of context: mobile, sensors, data and the future of privacy, Patrick Brewster Press.
Book length texts taking a critical perspective on data science in society
Broad, E. (2018). Made by humans: The AI condition. Melbourne Univ. Publishing.
Eggers, D. 2013, The circle, Random House, New York City, NY.
Gitelman, L. (Ed.) 2013,“Raw data” is an oxymoron, Massachusetts Institute of Technology, Boston, Massachusetts.
O'neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Pasquale, F. (2020). New Laws of Robotics. Harvard University Press.
Schneier, B. 2015, Data and Goliath: the hidden battles to collect your data and control your world, W. W. Norton & Company.
Book length texts exploring business innovation more generally
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.
Book length texts around data literacy and human cognition
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.
Kahneman, D. 2013, Thinking, fast and slow, Farrar, Straus and Giroux, New York City, NY.
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.
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.
Blogs
- https://towardsdatascience.com/
- https://www.ibm.com/blogs/journey-to-ai/
- https://www.r-bloggers.com/
Podcasts, radio shows, and other media
There are a plethora of podcast and video series on data science, these include (but are not limited to):
- Data Stori.es https://datastori.es/
- Women in Data Science https://www.widsconference.org/podcast.html
- The McKinsey on AI podcast https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/mckinsey-on-ai
- The range of podcasts in this list https://realpython.com/data-science-podcasts/
There are many accessible video talks on data science related themes, including many from TED.com You might find the following examples interesting:
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.