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): | IFN581 or IFN555 or IFQ555 or IFN556 or IFQ556 or IFN582 or IFN554 or IFQ554 or IFN557 or IFQ557 OR (192cps in IV04 or IV05 EV08 or EV07) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN10 or IN14 or IN23 or IN27 or PM20 or PV20 or PV21 or EN75 or EN76 or EN77). IFN619 can be enrolled in the same teaching period as IFN581 or IFN582. |
| Equivalent(s): | IFQ619 |
| Assumed Knowledge: | Some familiarity with simple coding or basic scripting to manipulate data is helpful. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $5,352 |
Unit Outline: Semester 1 2025, Gardens Point, Internal
| Unit code: | IFN619 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | IFN581 or IFN555 or IFQ555 or IFN556 or IFQ556 or IFN582 or IFN554 or IFQ554 or IFN557 or IFQ557 OR (192cps in IV04 or IV05 EV08 or EV07) OR (admission into IV54 or IV59 or IV58 or IV60) OR (admission into IN10 or IN14 or IN23 or IN27 or PM20 or PV20 or PV21 or EN75 or EN76 or EN77). IFN619 can be enrolled in the same teaching period as IFN581 or IFN582. |
| Equivalent: | IFQ619 |
| Assumed Knowledge: | Some familiarity with simple coding or basic scripting to manipulate data is helpful. |
| 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 strategic organisational decisions. Students will learn common methods for computational data 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 and addressing good questions that have practical value for organisations. Students will work with both structured and unstructured data, and will be encouraged to work with open data to address real-world 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 understanding of questions that needs answering by organisational stakeholders and computational approaches to addressing them. 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 high quality questions answerable with analytics within specific scenarios. Critical thinking about data analytics problems and stakeholder needs 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 Data Analytics with particular attention to human factors through relevant knowledge from the field of Cognitive Science.
Teaching will be delivered by a team with transdisciplinary expertise that covers the cognitive, informational and technological dimensions.
The unit will be delivered in a modular style with a mix of learning modes including opportunities for self-directed learning.
Students can learn basic software programming skills through participating in the unit, or can extend existing programming skills with data analytics specific technologies.
Assessment is designed to be an integral part of the learning process throughout the unit and includes formative feedback opportunities.
Feedback on Learning and Assessment
Lectures, 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: Foundational Techniques
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: Applied Data Analysis
Use data analytics notebook technology to address important questions associated with a specific application by selecting data, analysing with appropriate techniques, visualising the results, and documenting critical thinking and decision-making involved in the process including considerations of human factors and ethical principles.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Reflective Learning
The reflective learning assessment will document participation in the learning opportunities of the unit, and reflections on challenges encountered, how practice was or should have been changed, and strategies for
future learning and development.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
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.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
A personal computer device (preferably laptop) with internet access is required to access the provided data analytics infrastructure.
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: Foundational Techniques - 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: Applied Data Analysis - 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: Applied Data Analysis - 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: Applied Data Analysis - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Reflective Learning
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: Foundational Techniques - 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: Applied Data Analysis - 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: Applied Data Analysis - 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: Applied Data Analysis - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Reflective Learning
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: Foundational Techniques - 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: Applied Data Analysis - 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: Applied Data Analysis - Demonstrate ethical and socially responsible practice, recognising the importance of personal accountability, and reflective practice, risk-informed judgements, and leadership
Relates to: Reflective Learning
IN20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
Relates to: ULO1, ULO2, ULO4, Foundational Techniques, Applied Data Analysis - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: ULO1, ULO3, Foundational Techniques, Applied Data Analysis - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO2, ULO3, Foundational Techniques, Applied Data Analysis - Communicate effectively in IT professional and scholarly contexts to specialist and non-specialist audiences using written, visual and oral formats.
Relates to: ULO3, Foundational Techniques, Applied Data Analysis - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: ULO4, ULO5, Reflective Learning - Create positive change through critically reflecting upon and actioning responses to the social, cultural, ethical, sustainability, legal and accessibility issues in the IT field, including how they relate to First Nations Australians and diverse populations.
Relates to: ULO4, Applied Data Analysis
IN31 Master of Data Science
- Demonstrate advanced knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Foundational Techniques, Applied Data Analysis - Skilfully use appropriate statistical, computational, and modelling techniques to derive insights from data to support decision-making.
Relates to: Foundational Techniques, Applied Data Analysis - Critically apply specialist problem-solving approaches to design, execute and produce data science solutions.
Relates to: Foundational Techniques, Applied Data Analysis - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: Reflective Learning - Critically reflect on social and ethical data science issues, including how these relate to First Nations Australians.
Relates to: Applied Data Analysis