UniMate AI

DATA7002

数据科学

2 学分难度 超难👥 7 人学过

Gathering, understanding, interpreting and making decisions based on collected data is an invaluable tool for science, business and governments. Concerns about privacy, consent, confidentiality, discrimination, ownership, commercialisation, intellectual property and the importance of fair benefit sharing are known. Being aware of conflicts of interest and the need to ensure equity, reciprocity and respect for cultural diversity are increasingly seen as important. What is less recognised is the nature of the roles of those who access and make decisions about collected linked personal information. The emerging global banked data that has become a key part of contemporary decision-making raises questions about the role of the data scientist. In this course students will critically analyse the ethical and legal foundations of data science governance that are relevant to the technical processes of data collection, storage, exchange and access. Issues covered will include the ethical dimensions of data management, legal and regulatory frameworks in Australia and in relevant jurisdictions, data policy, data privacy, data ownership, legal liabilities regarding analytical decisions, and discrimination. The course will equip students to identify the ethical and legislative requirements that underpin the technical processes of data science and to apply ethical and legal considerations to the core processes of data analytics. It will also introduce algorithms and technical approaches to minimise the risk of data identifiability and disclosure. A range of case studies will be used to explore these issues in applications of data science, including the use of government administrative data for informing social policy, to integrate ethical, legal and technical considerations.

Syllabus

每周大纲

  1. 1

    Introduction and course foundations

    Official weekly topics for Week 1: - Lecture: Introduction and course foundations - Week one is an introduction to the course, including expectations regarding assessment. We will then consider how ethics is relevant to data science. An introduction to practical ethics and the nature of moral inquiry and philosophical analysis will follow. Learning outcomes: L01, L02, L03, L04 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  2. 2

    The theoretical tools of ethical analysis

    Official weekly topics for Week 2: - Lecture: The theoretical tools of ethical analysis - In the lecture this week we will consider ethical reasoning, some key approaches to philosophical ethics and then practical decision-making and problem solving. Over the next few weeks we will progress from personal ethics, to professional ethics, to large-scale challenges in our social system. Learning outcomes: L01, L02, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  3. 3

    Assessment Preparation and Case Studies; Responsibility

    Official weekly topics for Week 3: - Lecture: Assessment Preparation and Case Studies; Responsibility - How the major assessment pieces will function. Some examples of disputes in data science ethics, and how we might resolve them. The place of professionals in the social ecosystem. Learning outcomes: L01, L02, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  4. 4

    The Big Picture: socio-political factors

    Official weekly topics for Week 4: - Lecture: The Big Picture: socio-political factors - The politics of Big Data. Learning outcomes: L01, L02, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  5. 5

    Introduction to legal issues in Data Science

    Official weekly topics for Week 5: - Lecture: Introduction to legal issues in Data Science - Introduction to law and legal issues relevant to data science Introduction to law and data science What is law?, Jurisdiction, sources of law, and legal reasoning; law and technological change. Learning outcomes: L01, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  6. 6

    Intellectual Property and Contract Law

    Official weekly topics for Week 6: - Lecture: Intellectual Property and Contract Law - Intellectual property and contract law Copyright, patents, and trademarks Law of contracts Open access, open source, and open data Learning outcomes: L01, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  7. 7

    Privacy and Cybersecurity Law

    Official weekly topics for Week 7: - Lecture: Privacy and Cybersecurity Law - The concept of privacy Information privacy law Law and cybersecurity Learning outcomes: L01, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  8. 8

    Technical: Responsible statistical practice I

    Official weekly topics for Week 8: - Lecture: Technical: Responsible statistical practice I - Students will learn to recognize some common misuses of statistics through case studies involving various statistical techniques ranging from statistical graphics, hypothesis testing to regression models. Learning outcomes: L01, L04, L05, L06 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  9. 9

    Technical Strategies for Responsible DataSc: Responsible Statistical Practice Part II and Machine Learning Part I

    Official weekly topics for Week 9: - Lecture: Technical Strategies for Responsible DataSc: Responsible Statistical Practice Part II and Machine Learning Part I - The statistical framework Responsible statistical practice Part II: Students will learn general guidelines on responsible uses of statistics and how to do this. Responsible Machine Learning Part I: In this part of the course we will introduce the ethical considerations in machine learning research and practice. Learning outcomes: L01, L04, L05, L06 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  10. 10

    Responsible Machine Learning Part II

    Official weekly topics for Week 10: - Lecture: Responsible Machine Learning Part II - In this part of the course we will explore various biases and discrimination issues in machine learning and introduce actionable strategies to mitigate these biases and realize fairness-aware machine learning. Cognitive Biases, Human Biases in Machine Learning, Actionable Strategies to Mitigate Biases. Learning outcomes: L01, L04, L05, L06 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  11. 11

    Integrated seminar presentations

    Official weekly topics for Week 11: - Seminar: Integrated seminar presentations - Presentations will be assessed in the lecture and tutorial times of this week. Learning outcomes: L01, L02, L03, L04, L05, L06 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  12. 12

    Integrated seminar presentations

    Official weekly topics for Week 12: - Seminar: Integrated seminar presentations - Presentations will be assessed in the lecture and tutorial times of this week. Learning outcomes: L01, L02, L03, L04, L05, L06 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

  13. 13

    Future Directions and Course Summary

    Official weekly topics for Week 13: - Lecture: Future Directions and Course Summary - Discussion for final assessment, addressing what we have learned through the semester. Presentations may be assessed in the tutorial times of this week. Learning outcomes: L01, L02, L03, L04, L05 Official timetable activities: - Lecture | Thu 12:00 | 120 mins | 50-T105 Hawken Engineering Building, Learning Theatre - Tutorial | Fri 11:00 | 60 mins | 09-216 Michie Building, Seminar Room Source: 2026 S2 UQ Course Profile - https://course-profiles.uq.edu.au/course-profiles/DATA7002-61575-7560

Assessment

考核结构

Quizzes to be held in Tutorials Identity Verified In-person Online

Official due date: 8/08/2025 - 10/10/2025. Source: 2026 S2 UQ Course Profile.

20%

Plan for Major Assessment

Official due date: 26/09/2025 2:00 pm. Source: 2026 S2 UQ Course Profile.

10%

Group Presentation Team or group-based In-person

Official due date: 16/10/2025 - 31/10/2025. Source: 2026 S2 UQ Course Profile.

30%

Final Essay

Official due date: 3/11/2025 2:00 pm. Source: 2026 S2 UQ Course Profile.

40%

From Seniors

学长留下的

基础信息谁都查得到,真正值钱的是过来人的经验。

学姐说

比你早一年的学长留下的真实经验 —— ChatGPT 给不了。

这门课还没有学长经验,你可以是第一个 —— 注册后在课内分享。

往年考点 / 踩坑

这门课暂无往年考点记录。

毕业生去向(整体)

下面是匠人学院毕业生整体去过的公司分布(来自脱敏校友证言)。这是全平台的总体去向,不代表选这门课的人一定去这些公司。

统计自 313 份脱敏校友证言

Deloitte

6 位校友

岗位:Graduate Program · Graduate Consulting · Platform Engineer · Web developer · Platform engineer

Zerologix

4 位校友

岗位:Frontend Dev · junior frontend developer · Front-end Developer · Full Stack Developer

Servian

4 位校友

岗位:Full-stack Developer · Data Engineer · Consultant

关于这块数据,我们说实话

雇主墙来自脱敏毕业生证言(testimonials)的整体分布,无法关联到具体学员或其所选课程;仅作为毕业生去向的总体社会证明展示。

我们没有"某位学长选了这门课、后来进了哪家公司"这种可查询的个人去向档案 —— 校友证言是脱敏的,无法关联到具体的人或他选过的课。所以这里只给整体分布,不给个人路径,不编。