DATA471-19S1 (C) Semester One 2019

Special Topic: The Trustworthy Data Scientist

15 points
Details:
Start Date: Monday, 18 February 2019
End Date: Sunday, 23 June 2019
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 1 March 2019
  • Without academic penalty (including no fee refund): Friday, 10 May 2019

Description

This course will stimulate students to think about the ethical facets of their data scientific projects and provide them with conceptual and practical tools to assess said project. The ethics and security of data collection, storage, manipulation, analysis and communication is of paramount importance in our information based society. This course faces these topics from the point of view of data scientists rather than consumers or data subjects enabling the student to become trustworthy professionals. The students will learn to identify risk and opportunities related to fairness, agency, interpretability, and security. Maori Data Sovereigneity, Te Mana Raraunga, and its relevance for data scientist in New Zealand will be introduced. The course will follow a flipped class-room flow. Fundamental concepts will be first introduce via guided discussions and hands-on-data exercises during the laboratories. In the lectures, the understanding of concepts and tools introduced in the laboratories is made rigorous and generalised. The course will provide a safe and supportive environment in which students can express their ideas, explore their ethical frameworks and collaborate to find common ground. Students will need to be familiar with basic data science concepts and techniques.

DATA471 will stimulate students to think about the ethical facets of their data scientific projects and provide them with conceptual and practical tools to assess the projects. The ethics and security of data collection, storage, manipulation, analysis and communication is of paramount importance in our information based society. This course faces these topics from the point of view of data scientists—rather than consumers or data subjects—enabling the student to become trustworthy professionals. The students will learn to identify risk and opportunities related to fairness, agency, interpretability, and security. Māori Data Sovereigneity, Te Mana Raraunga, and its relevance for data scientist in New Zealand will be introduced. The course will follow a flipped class-room flow. Fundamental concepts will be first introduce via guided discussions and hands-on-data exercises during the laboratories. In the lectures, the understanding of concepts and tools introduced in the laboratories is made rigorous and generalised.

The course is run thanks to a number of important collaborations: Te Mana Raraunga will provide essential insight on Maori Data Sovereignty; Data 4 Democracy is providing a much needed ethical framework for data scientists.

The course will provide a safe and supportive environment in which students can express their ideas, explore their ethical frameworks and collaborate to find common ground. Students will need to be familiar with basic data science concepts and techniques.

Learning Outcomes

  • At the end of this course, students will be able to:
  • Identify ethical risks (biases, privacy violations, …) in data science projects.
  • Prevent, mitigate, or remediate unethical data science projects.
  • Perform rudimentary data anonymization techniques and explain their importance.
  • Understand other stakeholders’ ethical requests.
  • Identify when Te Mana Raraunga is relevant in a data science project.

Pre-requisites

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

Timetable 2019

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 10:00 - 11:00 Jack Erskine 445 18 Feb - 7 Apr
29 Apr - 2 Jun
Computer Lab A
Activity Day Time Location Weeks
01 Monday 09:00 - 11:00 Jack Erskine 244 (18/2-25/2, 11/3-25/3, 29/4, 13/5-27/5)
Jack Erskine 442 (6/5)
18 Feb - 3 Mar
11 Mar - 31 Mar
29 Apr - 2 Jun

Course Coordinator / Lecturer

Giulio Dalla Riva

Assessment

- Active engagement 30%
- Three assignments 40%
- Group project 30%

Indicative Fees

Domestic fee $923.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All DATA471 Occurrences

  • DATA471-19S1 (C) Semester One 2019