QUT009 QUT You: Data Science for Society
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: | QUT009 |
|---|---|
| Credit points: | 6 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $277 |
| Domestic tuition unit fee | $1,662 |
| International unit fee | $2,148 |
Unit Outline: Semester 2 - 6 Week C 2024, Gardens Point, Internal
| Unit code: | QUT009 |
|---|---|
| Credit points: | 6 |
| Coordinator: | David Warne | david.warne@qut.edu.au |
Overview
Data is part of the fabric of our modern societies with almost all aspects of our lives influenced, for better or worse, by systems that are fundamentally data-driven. As individuals, we often unknowingly contribute enormous quantities of data to these systems through our use of smart devices, wearables, and online platforms. Understanding the power and limitations of the rapidly growing field of data science is more important than ever before.
In this unit, you will identify sources of bias, error, and misinterpretation within the data science pipeline and the potential consequences of data-driven decision-making if these sources are left unchecked. This grounding in fundamental principles of data science will empower you to think critically and ethically about these systems and how they affect us. Regardless of your career or discipline, you have a role to play in ensuring data-driven systems are built that align with our personal values and the values of our society.
Learning Outcomes
On successful completion of this unit you will be able to:
- Identify and critique common misuses of data science.
- Identify the different components of a data science pipeline and analyse the social and ethical impact it could have on relevant stakeholders, users, and communities.
Content
The unit will introduce key issues surrounding data science in society:
- What is data science and how is it used in our society? What are some potential benefits or harms in society arising from data-driven systems?
- Understand the data science pipeline and potential sources of error, bias and unintended outcomes.
- Discover and discuss key ethical considerations surrounding each stage of the data science pipeline. Understand how these issues disproportionally affect underrepresented groups in society.
- Understand common misconceptions, myths and misunderstandings of data science. Learn to identify these issues in real world applications.
- Explore examples of data science application in the real-world including case studies highlighting unintended consequences of data-driven systems.
- Develop an awareness of issues surrounding data-driven systems to enable informed choices in and around our sharing and use of data.
Learning Approaches
This unit will take a case-study based approach to learning. Each week you will examine the application of data science across a range of disciplines to explore the key themes of the unit. You will engage in weekly 2-hour interactive workshops to collaboratively explore the data science pipeline in real-world contexts. You will also complete weekly online materials, such as videos, readings and interactive activities to expand your knowledge and apply this to different case studies. The assessment task will provide you with an opportunity to explore the broader implications of data science on society and your future workplaces. You can expect to spend on average 12 hours per week involved in preparing for and attending all scheduled workshops, completing online modules and assessment tasks and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
You will receive formative feedback from teaching staff and your peers during workshops as well as by engaging in the interactive online learning resources that will provide you with instant feedback on your knowledge of the unit. You will also have an opportunity to gain peer and informal teaching staff feedback during the early stages of your assessment development. The workshop activities and assessment will encourage you to actively reflect on the role of data science in your personal and professional lives.
Assessment
Overview
In this unit, you will complete a workbook throughout the teaching period that requires you to evaluate the application and implications of different uses of data science both within and outside your chosen discipline. You will gain feedback on these tasks during the tutorials with the support of your teaching team and peers. At the end of the unit, you will submit your complete workbook for evaluation.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Data Science Workbook
You will complete several tasks over the course of the unit that evaluates your understanding of the fundamental principles of data science, its applications, and surrounding issues of bias, error and ethical decision making. Tasks will include:
1) Personal reflection on the application of data science within your own context or field of interest;
2) Critical analysis of data science applications in real research studies;
3) Identification of potential bias, error and misinterpretation, and suggestion of mitigations or corrections;
4) Discussion and analysis of data science scenarios using various ethical frameworks;
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.
Risk Assessment Statement
During this unit, you will discuss sensitive topics with your peers relating to the application of data science. The teaching team will support you to develop the appropriate skills to engage in these conversations in an appropriate and respectful manner to ensure a positive experience for all students.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Relates to: Data Science Workbook
2: Engineering Application Ability
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Relates to: Data Science Workbook
3: Professional and Personal Attributes
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Unit Outline: Semester 2 - 6 Week C 2024, Kelvin Grove, Internal
| Unit code: | QUT009 |
|---|---|
| Credit points: | 6 |
| Coordinator: | David Warne | david.warne@qut.edu.au |
Overview
Data is part of the fabric of our modern societies with almost all aspects of our lives influenced, for better or worse, by systems that are fundamentally data-driven. As individuals, we often unknowingly contribute enormous quantities of data to these systems through our use of smart devices, wearables, and online platforms. Understanding the power and limitations of the rapidly growing field of data science is more important than ever before.
In this unit, you will identify sources of bias, error, and misinterpretation within the data science pipeline and the potential consequences of data-driven decision-making if these sources are left unchecked. This grounding in fundamental principles of data science will empower you to think critically and ethically about these systems and how they affect us. Regardless of your career or discipline, you have a role to play in ensuring data-driven systems are built that align with our personal values and the values of our society.
Learning Outcomes
On successful completion of this unit you will be able to:
- Identify and critique common misuses of data science.
- Identify the different components of a data science pipeline and analyse the social and ethical impact it could have on relevant stakeholders, users, and communities.
Content
The unit will introduce key issues surrounding data science in society:
- What is data science and how is it used in our society? What are some potential benefits or harms in society arising from data-driven systems?
- Understand the data science pipeline and potential sources of error, bias and unintended outcomes.
- Discover and discuss key ethical considerations surrounding each stage of the data science pipeline. Understand how these issues disproportionally affect underrepresented groups in society.
- Understand common misconceptions, myths and misunderstandings of data science. Learn to identify these issues in real world applications.
- Explore examples of data science application in the real-world including case studies highlighting unintended consequences of data-driven systems.
- Develop an awareness of issues surrounding data-driven systems to enable informed choices in and around our sharing and use of data.
Learning Approaches
This unit will take a case-study based approach to learning. Each week you will examine the application of data science across a range of disciplines to explore the key themes of the unit. You will engage in weekly 2-hour interactive workshops to collaboratively explore the data science pipeline in real-world contexts. You will also complete weekly online materials, such as videos, readings and interactive activities to expand your knowledge and apply this to different case studies. The assessment task will provide you with an opportunity to explore the broader implications of data science on society and your future workplaces. You can expect to spend on average 12 hours per week involved in preparing for and attending all scheduled workshops, completing online modules and assessment tasks and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
You will receive formative feedback from teaching staff and your peers during workshops as well as by engaging in the interactive online learning resources that will provide you with instant feedback on your knowledge of the unit. You will also have an opportunity to gain peer and informal teaching staff feedback during the early stages of your assessment development. The workshop activities and assessment will encourage you to actively reflect on the role of data science in your personal and professional lives.
Assessment
Overview
In this unit, you will complete a workbook throughout the teaching period that requires you to evaluate the application and implications of different uses of data science both within and outside your chosen discipline. You will gain feedback on these tasks during the tutorials with the support of your teaching team and peers. At the end of the unit, you will submit your complete workbook for evaluation.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Data Science Workbook
You will complete several tasks over the course of the unit that evaluates your understanding of the fundamental principles of data science, its applications, and surrounding issues of bias, error and ethical decision making. Tasks will include:
1) Personal reflection on the application of data science within your own context or field of interest;
2) Critical analysis of data science applications in real research studies;
3) Identification of potential bias, error and misinterpretation, and suggestion of mitigations or corrections;
4) Discussion and analysis of data science scenarios using various ethical frameworks;
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.
Risk Assessment Statement
During this unit, you will discuss sensitive topics with your peers relating to the application of data science. The teaching team will support you to develop the appropriate skills to engage in these conversations in an appropriate and respectful manner to ensure a positive experience for all students.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Relates to: Data Science Workbook
2: Engineering Application Ability
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Relates to: Data Science Workbook
3: Professional and Personal Attributes
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Unit Outline: Semester 2 - 6 Week C 2024, Online
| Unit code: | QUT009 |
|---|---|
| Credit points: | 6 |
Overview
Data is part of the fabric of our modern societies with almost all aspects of our lives influenced, for better or worse, by systems that are fundamentally data-driven. As individuals, we often unknowingly contribute enormous quantities of data to these systems through our use of smart devices, wearables, and online platforms. Understanding the power and limitations of the rapidly growing field of data science is more important than ever before.
In this unit, you will identify sources of bias, error, and misinterpretation within the data science pipeline and the potential consequences of data-driven decision-making if these sources are left unchecked. This grounding in fundamental principles of data science will empower you to think critically and ethically about these systems and how they affect us. Regardless of your career or discipline, you have a role to play in ensuring data-driven systems are built that align with our personal values and the values of our society.
Learning Outcomes
On successful completion of this unit you will be able to:
- Identify and critique common misuses of data science.
- Identify the different components of a data science pipeline and analyse the social and ethical impact it could have on relevant stakeholders, users, and communities.
Content
The unit will introduce key issues surrounding data science in society:
- What is data science and how is it used in our society? What are some potential benefits or harms in society arising from data-driven systems?
- Understand the data science pipeline and potential sources of error, bias and unintended outcomes.
- Discover and discuss key ethical considerations surrounding each stage of the data science pipeline. Understand how these issues disproportionally affect underrepresented groups in society.
- Understand common misconceptions, myths and misunderstandings of data science. Learn to identify these issues in real world applications.
- Explore examples of data science application in the real-world including case studies highlighting unintended consequences of data-driven systems.
- Develop an awareness of issues surrounding data-driven systems to enable informed choices in and around our sharing and use of data.
Learning Approaches
This unit will take a case-study based approach to learning. Each week you will examine the application of data science across a range of disciplines to explore the key themes of the unit. You will engage in weekly 2-hour interactive workshops to collaboratively explore the data science pipeline in real-world contexts. You will also complete weekly online materials, such as videos, readings and interactive activities to expand your knowledge and apply this to different case studies. The assessment task will provide you with an opportunity to explore the broader implications of data science on society and your future workplaces. You can expect to spend on average 12 hours per week involved in preparing for and attending all scheduled workshops, completing online modules and assessment tasks and undertaking your own independent study to consolidate your learning.
Feedback on Learning and Assessment
You will receive formative feedback from teaching staff and your peers during workshops as well as by engaging in the interactive online learning resources that will provide you with instant feedback on your knowledge of the unit. You will also have an opportunity to gain peer and informal teaching staff feedback during the early stages of your assessment development. The workshop activities and assessment will encourage you to actively reflect on the role of data science in your personal and professional lives.
Assessment
Overview
In this unit, you will complete a workbook throughout the teaching period that requires you to evaluate the application and implications of different uses of data science both within and outside your chosen discipline. You will gain feedback on these tasks during the tutorials with the support of your teaching team and peers. At the end of the unit, you will submit your complete workbook for evaluation.
Unit Grading Scheme
S (Satisfactory) / U (Unsatisfactory)
Assessment Tasks
Assessment: Data Science Workbook
You will complete several tasks over the course of the unit that evaluates your understanding of the fundamental principles of data science, its applications, and surrounding issues of bias, error and ethical decision making. Tasks will include:
1) Personal reflection on the application of data science within your own context or field of interest;
2) Critical analysis of data science applications in real research studies;
3) Identification of potential bias, error and misinterpretation, and suggestion of mitigations or corrections;
4) Discussion and analysis of data science scenarios using various ethical frameworks;
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.
Risk Assessment Statement
During this unit, you will discuss sensitive topics with your peers relating to the application of data science. The teaching team will support you to develop the appropriate skills to engage in these conversations in an appropriate and respectful manner to ensure a positive experience for all students.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Relates to: Data Science Workbook
2: Engineering Application Ability
Relates to: Data Science Workbook
Relates to: Data Science Workbook
Relates to: Data Science Workbook
3: Professional and Personal Attributes
Relates to: Data Science Workbook
Relates to: Data Science Workbook