DATA416-22S1 (C) Semester One 2022

Contemporary Issues in Data Science

15 points

Details:
Start Date: Monday, 21 February 2022
End Date: Sunday, 26 June 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 6 March 2022
  • Without academic penalty (including no fee refund): Sunday, 15 May 2022

Description

This course focuses on the technical challenges in data science that societal and regulatory actions pose. It aims to introduce students the often very different and sometimes even conflicting perspectives from which policymakers and the technical community approaches these problems. We will review and discuss different examples from different areas of data science such as the extent to which machine learning and deep learning techniques conform with GDPR regulations on transparency, explainability, and accountability; impossibility theorems showing off the limits of data science methods; the mathematical foundations and data science techniques for mechanism design in order to manipulate beliefs (represented as transitive, anti-symmetric, and complete binary relations); and provide students as potential future product developers with the necessary knowledge to engage in responsible product development practices that are informed by regulatory requirements and expectations. This course develops students' understanding of the role of data science in decision making and the impact of data science in the design of AI systems. The course reflects the main issues of controversy identified in international policy debates.

This course develops students’ understanding of the role of data science in decision making and the impact of data science in the design of AI systems. The course reflects the main issues of controversy identified in international policy debates.

Learning Outcomes

  • On successful completion of this course, students will have:
  • An overall understanding of data science principles in AI and various applications across different domains.
  • An understanding of key theoretical foundations and concepts concerning data science research and development, as well as deployment of AI technologies.  
  • Developed a general awareness of data science techniques and international guidelines for designing new AI systems.
  • Acquired the capacity to work independently and manage their time in order to meet course deadlines. Students will develop valuable writing and presentation skills.
    • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Critically competent in a core academic discipline of their award

      Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

      Employable, innovative and enterprising

      Students will develop key skills and attributes sought by employers that can be used in a range of applications.

      Biculturally competent and confident

      Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.

      Globally aware

      Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.

Prerequisites

Subject to approval of the Head of Department of Mathematics and Statistics.

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Thursday 11:00 - 13:00 Jack Erskine 242
21 Feb - 10 Apr
2 May - 5 Jun
Tutorial A
Activity Day Time Location Weeks
01 Friday 10:00 - 11:00 James Logie 214-Mac Computer Lab
7 Mar - 10 Apr
2 May - 5 Jun

Course Coordinator / Lecturer

Gabor Erdelyi

Indicative Fees

Domestic fee $969.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All DATA416 Occurrences

  • DATA416-22S1 (C) Semester One 2022
  • DATA416-22S1 (D) Semester One 2022 (Distance)