IFN619 Data Analytics for Strategic Decision Makers
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IFN619 |
|---|---|
| Prerequisite(s): | ((IFN501 or (IFN555 and IFN556) or (IFQ555 and IFQ556) or IFN503 or (IFN551 and IFN553) or (IFQ551 and IFQ553)) or (enrolment in IN14 or IQ14 or IN23 or IN26 or IN27 or IV53 or IV54 or IV55 or IV56 or IV58) or (192cps in SV03, IV04, MV05, BV06 or EV08) |
| Equivalent(s): | IFQ619 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,528 |
| International unit fee | $4,824 |
Unit Outline: Semester 1 2024, Gardens Point, Internal
| Unit code: | IFN619 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFN501 or (IFN555 and IFN556) or (IFQ555 and IFQ556) or IFN503 or (IFN551 and IFN553) or (IFQ551 and IFQ553)) or (enrolment in IN14 or IQ14 or IN23 or IN26 or IN27 or IV53 or IV54 or IV55 or IV56 or IV58) or (192cps in SV03 or IV04 or MV05 or BV06 or EV08). |
| Equivalent: | IFQ619 |
| Coordinator: | Andrew Gibson | andrew.gibson@qut.edu.au |
Overview
This unit offers students a practical introduction to the field of data analytics, and its application to making decisions. Students will learn common methods for quantitative and computational analytics, through which they can gain an overview of key concepts, skills, and technologies for sourcing data, performing data analysis, and producing appropriate visualisations. While the course covers relevant technologies for data analytics and information visualisation, the focus is on asking the right questions and solving related problems which are driven from the business/organisational perspective. Students will work with both structured and unstructured data, and will be encouraged to work with open data to address real-life problems in ways that align with ethical principles and good data governance.
Learning Outcomes
On successful completion of this unit you will be able to:
- Interpret human information problems from an analytical perspective, with a focus on asking questions relevant to strategic decision making.
- Select and apply a range of data analysis techniques on diverse data sources to address organisational concerns.
- Synthesise relevant data using appropriate analytical and visualisation techniques in a way that provides useful insight for organisations.
- Integrate knowledge of human factors into the data analysis process, so as to apply ethical principles.
- Reflect on personal capabilities and appraise oneself in relation to expectations for information professionals.
Content
Unit content will be focused on improving your understanding of the relationship between data analytics and organisational insight. This includes an examination of cognitively motivated language analytics. It will also expose you to the diversity of data relevant to information organisations, and a variety of analysis and visualisation techniques that can be used to extract insight.
Students will be encouraged to consider the right questions to be answered with analytics within specific scenarios. Critical thinking about data analytics problems will be a central thread of the unit.
Content will also enable you to explore some of the human factors involved in data analytics for organisations such as trust, privacy, transparency and ethical use of data.
Learning Approaches
This unit takes a contextualised practice approach to Decision Science and why it is important. Practical activities address these concepts within a socio-technical context.
Teaching will be delivered by a team with transdisciplinary expertise that covers the cognitive, informational and technological dimensions.
Conceptual material on how a cognitive understanding of interactions can improve interactive augmented intelligence systems will be presented in parallel to the practical application and development of techniques for interactive technologies.
The unit will be delivered in a modular style with a focus on collaborative approaches to learning, and a mixture of online and face to face activities.
Assessment is considered to be an integral part of the learning in the unit and provides opportunity for formative feedback.
Feedback on Learning and Assessment
Studio workshops, tutorials and drop-in sessions will include opportunities for discussion and receiving immediate feedback on ideas related to the conceptual content.
Practical opportunities will be provided for the teaching team to view your work and provide direct feedback on it. You will be encouraged to use this feedback to enhance your opportunity for success in graded assessment tasks.
The teaching team will monitor the cohort as a whole and provide ongoing feedback throughout the semester on general progress of the cohort, or addressing specific issues that arise during the unit.
Individual feedback will be provided between assessment tasks to allow improvement over the course of the
semester.
Detailed criteria sheets with any relevant comments will be provided for all assessment.
Opportunities will be provided on key tasks to receive preliminary criteria-based feedback without impact to your final grade.
Opportunities will be provided for peer feedback to enhance the authenticity of assessment tasks, and encourage engagement with significant themes.
Opportunities will be provided for self-reflection to integrate learning, feedback and self assessment.
Assessment
Overview
The assessment for this unit is designed to integrate conceptual material on Cognition, Information Interaction, and Information Technologies, within a practical context. Two assessment tasks focus on foundational knowledge and skills, critical understanding of knowledge in context, and application of knowledge. One task will focus on self-reflection. Foundational knowledge and self-reflection tasks will include formative components. All tasks are criteria referenced.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Knowledge and Skills Task
Use data analytics notebook technology to address important questions by selecting data, analysing with appropriate techniques, and visualising the results.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Synthesis and Application Task
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Reflective Practice Journal
The reflective journal will document challenges encountered during the unit, how practice was or should have been changed to overcome them, and strategies for
future learning and development.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no extraordinary risks associated with the classroom/lecture activities in this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN75 Master of Sustainable Infrastructure with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Data Analytics domains
Relates to: Knowledge and Skills Task - Analyse and evaluate problems in Sustainable Infrastructure and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Synthesis and Application Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Synthesis and Application Task - Effectively communicate problems in Sustainable Infrastructure and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Synthesis and Application Task - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Reflective Practice Journal
EN76 Master of Renewable Energy with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Renewable Energy and Data Analytics domains
Relates to: Knowledge and Skills Task - Analyse and evaluate problems in Renewable Energy and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Synthesis and Application Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Renewable Energy and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Synthesis and Application Task - Effectively communicate problems in Renewable Energy and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Synthesis and Application Task - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Reflective Practice Journal
EN77 Master of Advanced Manufacturing with Data Analytics
- Demonstrate and apply advanced and specialist theory-based discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Advanced Manufacturing and Data Analytics domains
Relates to: Knowledge and Skills Task - Employ advanced specialist technical skills, analysis approaches, design, and data to the solution of problems in Advanced Manufacturing and Data Analytics domains, critically evaluating solutions and practice-informed performance to deliver whole of life requirements and strategic objectives
Relates to: Synthesis and Application Task - Implement professional communication and collaborative skills while engaging with stakeholders, exchanging ideas, and presenting complex information to specialist and non-specialist audiences in Advanced Manufacturing and Data Analytics domains
Relates to: Synthesis and Application Task - Demonstrate ethical and socially responsible practice, recognising the importance of personal accountability, and reflective practice, risk-informed judgements, and leadership
Relates to: Reflective Practice Journal