69367 Science and Big Data
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Credit points: 6 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Description
Data is everywhere and part of our daily lives in more ways than most realise. The amount of digital data that exists is growing exponentially; according to estimates, global creation of data will top 180 zettabytes by 2025. Big data promises to revolutionise the production of knowledge within and beyond science, by enabling novel and highly efficient ways to plan, conduct, disseminate and assess research. Innovative research that leverages big data can dramatically advance the fields of science but can also raise new ethical challenges for managers.
This subject explores issues around the analysis and management of big data. Students learn basic data analysis using both graphical and statistical techniques. Also explored are issues relating to privacy and the ethical management of big data sets, for example in the context of personal health, ecology and sustainability. The course also introduces students to questions of sovereignty and informed consent as relating to areas concerning Indigenous Australians.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Conduct analyses and interpret findings to leverage data for science applications. |
|---|---|
| 2. | Articulate key governance and accountability requirements for interconnected science data management, including privacy and ethics. |
| 3. | Evaluate the critical importance of adhering to Indigenous Data Sovereignty (IDS) and appropriate privacy protocols for working with and for Indigenous groups. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Critically appraise, synthesise and apply advanced skills and knowledge to contribute to professional practice and scholarship relevant to sustainability and leadership. (1.1)
- Analyse data and information to design, disseminate and conduct appropriate independent research to solve complex sustainability and leadership problems. (2.1)
- Develop, prepare, engage, lead, reflect and report upon work and leadership practices adhering to ethical conduct, risk management, organisation, and governance or regulatory requirements in the context of sustainability. (3.1)
- Present and justify complex ideas around sustainability and leadership independently or in collaborative contexts using various communication approaches from a variety of methods (oral, written, digital and visual) to discipline experts, policy makers, consumers, scientists, industry, and the community. (5.1)
- Critically reflect on Indigenous Australian contexts to inform professional cultural capability to work effectively with and for, Indigenous Australians within sustainability and leadership. (6.1)
Contribution to the development of graduate attributes
1. Disciplinary knowledge. Data analysis requires skills in the disciplines of computing and statistics. Students will be introduced to these areas and learn how to analyse data graphically using simple plotting techniques such as bar charts and scatter plots through to more sophisticated tools such as boxplots, histograms and QQ plots. Students will also learn how to analyse data statistically, starting with basic techniques to compare means through to more complex tools to model relationships between variables. Skills learned will be assessed though Assessment Task 1 and 3.
2. Research, inquiry and critical thinking. At the heart of much research is the analysis of data sets, some big and some small. The nature of such analysis is inquiry-based and the role of critical thinking is in drawing conclusions from such analysis. Students will develop skills in these areas by analysing data drawn from a variety of real-world studies. Abilities acquired here will be assessed with Assessment Tasks 2 and 3.
3. Professional, ethical and social responsibility. Big data sets contain wide varieties of different types of data, some of which may be highly personal in nature or sensitive for commercial, legal or security reasons. Those accessing, managing or analysing such big data sets therefore have certain prescribed or inferred responsibilities with respect to this data. Students will explore these issues, particularly those relating to ethics and expectations of privacy in the context of health and medical data sets. The issues explored will be assessed with Assessment Tasks 1 and 2.
5. Communication. Extracting meaningful information from big data sets often requires use of techniques that can be quite technical in nature and involve language specific to the field. Those working in data management and analysis need to be able to us these techniques, but they must also be able to explain outcomes and conclusions drawn using language that is understand by all. In this context, effective communication is the ability to understand a piece of technical analysis and be able to relate this using non-technical language. Abilities acquired here will be assessed through Assessment Task 3.
6. Aboriginal and Torres Strait Islander Knowledge and Connection with Country. An understanding of Indigenous Data Sovereignty and related privacy protocols is critical in any professional setting that involves collection and use of data for scientific purposes. Students gain an understanding of how to comply with these protocols when working for and with Indigenous Australians, and how to apply this learning in their own professional setting. The issues explored will be assessed through Assessment Task 2.
Teaching and learning strategies
This subject is delivered online with all subject material provided through Canvas. Each week, students will engage with the subject material, working through the prepared content and examples. Students will acquire skills in data analysis through interpretation of output from statistical software using real-world data sets. Examples will be used to illustrate the importance of privacy and ethical considerations as relating to big data, and the particular concerns relating to issues of Indigenous data sovereignty and free, prior and informed consent.
Assessments in this course are thoughtfully designed to complement student learning, providing them with a chance to apply and monitor their skill development and grasp of essential concepts. Students will receive formative feedback on their work throughout the session.
Content (topics)
The subject is broken up into 6 modules:
1. Introduction to big data.
2. Data collection and preparation
3. Handling and storing data
4. Using data
5. Interpreting data.
6. Emerging technologies
Assessment
Assessment task 1: Quiz
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary Knowledge 3. Professional, ethical and social responsibility |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1 and 2 This assessment task contributes to the development of course intended learning outcome(s): 1.1 and 3.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 10% |
| Criteria: | Accuracy of submitted answers. |
Assessment task 2: Industry evaluation
| Intent: | This assessment task contributes to the development of the following graduate attributes: 2. Research, inquiry and critical thinking 3. Professional, ethical and social responsibility 6. Aboriginal and Torres Strait Islander Knowledge and Connection with Country |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 2.1, 3.1 and 6.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 40% |
| Criteria: | A marking criteria and expected engagement with the discussion board including peer review and protocol will be provided on the CANVAS site |
Assessment task 3: Critical analysis report
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1. Disciplinary knowledge 2. Research, inquiry and critical thinking 5. Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1 and 2 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 5.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | Detailed assessment criteria are available on Canvas,and will be used for marking the reports and provides details on how this assessment achieves the graduate attributes. This means that you must access the criteria required before you begin the assessment. Submitted work will be marked against the marking rubric provided in Canvas. |
Minimum requirements
Students must receive 50% of all available marks in order to pass this subject.