Introduction to Data Science, Ethics, and Privacy

Postgraduate | 2026

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Area/Catalogue
COMP 6005
Course ID icon
Course ID
201678
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
2
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

This course introduces students to the foundational concepts of data science, focusing on the ethical, legal, and privacy considerations relevant to the field. Students will gain a basic understanding of the key principles of data privacy and security, as well as the ethical implications of collecting, handling, and analysing data. Through practical exercises and case studies, students will develop the skills to apply ethical frameworks and basic data management techniques in simple real-world contexts. This course aims to build students’ confidence in recognising and addressing ethical and privacy challenges in data science, equipping them with the knowledge and tools to manage data responsibly while adhering to professional standards.

Course learning outcomes

  • Describe core ethical principles and legal frameworks in data science, including fairness, transparency, accountability, and data privacy
  • Identify and mitigate ethical concerns and data privacy risks in real-world data science applications
  • Apply ethical frameworks to evaluate data science projects, balancing ethical considerations with project goals
  • Collaborate effectively in teams to develop inclusive and responsible data science workflows, addressing ethical and legal challenges
  • Reflect on the cultural and societal impacts of data science technologies and incorporate cultural sensitivity in the development and deployment of data-driven systems

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A