Program Overview

Course Overview

The "Data Feminism" course is designed to bridge the gap between data science and the crucial aspects of equality, diversity, and equitable conditions. With a comprehensive exploration of these themes, the course delves deeply into both theoretical concepts and technical considerations surrounding data ethics, data justice, and data sustainability.

Course Description

The course is mainly inspired by the book "Data Feminism", which presents a paradigm that re-imagines the concept of data and its applications while acknowledging the inherent power imbalances within data science. Upon completing the course, students will be able to use data and data science to challenge and mitigate injustices amplified by data-driven practices. Moreover, they will gain the analytical skills to identify and address biases inherent in various data science practices.

Information per Course Offering

• Termin: Autumn 2025
• Course location: KTH Campus
• Duration: 25 Aug 2025 - 12 Jan 2026
• Periods: Autumn 2025: P1 (4 hp), P2 (3.5 hp)
• Pace of study: 25%
• Application code: 10436
• Form of study: Distance Daytime
• Language of instruction: English
• Number of places: Places are not limited
• Target group: No information inserted
• Planned modular schedule: No information inserted
• Schedule: Schedule is not published
• Part of programme: No information inserted

Course Syllabus

The course syllabus is available in an accessible format on this page.

Content and Learning Outcomes

Course Disposition

The course contains seven modules. Within each module, students will engage in two sessions: one lecture and one discussion.

Course Contents

This course aims to bridge ethical and social justice themes with advancements in data science, exploring how individuals working with data can actively challenge and transform power differentials through an intersectional feminism lens. The objectives are mainly drawn based on the seven principles outlined in the book "Data Feminism".

Intended Learning Outcomes

After the course, the student should be able to:

1. Analyze the theoretical and technical issues related to data ethics, data justice, and data sustainability.
2. Apply acquired knowledge to employ data and data science as tools to confront injustices magnified by data and associated techniques.
3. Evaluate data science practices by recognizing their biases and taking actions to address them.

Literature and Preparations

Specific Prerequisites

Participants should be enrolled as doctoral students.

Recommended Prerequisites

The students should be familiar with Python programming and have completed courses on data science or deep learning.

Literature

Information about course literature can be found in the course memo for the course offering or in the course room in Canvas.

Examination and Completion

Grading Scale

P, F

Examination

The assessment of this course will be based on four distinct tasks:

1. Task 1 (reading assignments): Each student/group is required to submit a comprehensive review for a set of assigned papers corresponding to each module.
2. Task 2 (presentation): Each student/group should present a set of the assigned papers.
3. Task 3 (group discussion): Students are expected to attend the group presentation sessions and actively engage in the subsequent group discussions.
4. Task 4 (final project): The final project requires each student/group to reproduce a paper relevant to the course topics and deliver an oral presentation.

Further Information

Course Room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas.

Offered by

EECS/Software and Computer Systems

Education Cycle

Third cycle

Supplementary Information

None

Postgraduate Course

Postgraduate courses at EECS/Software and Computer Systems