Human Centered Data Science S22
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Description

In recent years, data science has developed rapidly, primarily due to the progress in machine learning. This development has opened up new opportunities in a variety of social, scientific, and technological areas. From the experience of recent years, however, it is becoming increasingly clear that the concentration on purely statistical and numerical aspects in data science fails to capture social nuances or take ethical criteria into account. The research area Human-Centered Data Science closes this gap at the intersection of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Human Computation, and the statistical and numerical techniques of Data Science.

Human-Centered Data Science (HCDS) focuses on fundamental principles of data science and its human implications, including research ethics, data privacy, legal frameworks, algorithmic bias, transparency, fairness, and accountability, data provenance, curation, preservation, and reproducibility, user experience design and research for big data, human computation. The course also teaches effective oral, written, and visual scientific communication as well as skills to reflect on the societal impacts of data science.

At the end of the course we expect students be able to

  • apply human-centered design methods in the data science practice, including ethical concerns and privacy requirements,
  • build reproducible data science workflow,
  • know how to differentiate the terms bias, fairness, accountability, transparency and explanations,
  • apply measures, techniques and frameworks on human-centered explainable AI (XAI),
  • responsibly integrate human labor, i.e., crowdsourcing, in data science projects, and
  • augment data science workflows by qualitative research approaches.

At the end of this course, students will understand the main concepts, theories, practices, and different perspectives on which data can be collected and manipulated. Furthermore, students will be able to realize the impact of current technological developments may have on society.

Please note, this course curriculum was initially developed by Jonathan T. Morgan, Cecilia Aragon, Os Keyes, and Brock Craft. We have adapted the curriculum for the European context and our specific understanding of the field.

Here you can find our Code of Conduct.

Basic Course Info

Course No Course Type Hours
19331101 Vorlesung 2
19331102 Übung 2

Time Span 21.04.2022 - 21.07.2022
Instructors
Claudia Müller-Birn
Lars Sipos

Study Regulation

0086c_k150 2014, BSc Informatik (Mono), 150 LPs
0086d_k135 2014, BSc Informatik (Mono), 135 LPs
0087d_k90 2015, BSc Informatik (Kombi), 90 LPs
0088d_m60 2015, MSc Informatik (Kombi), 60 LPs
0089b_MA120 2008, MSc Informatik (Mono), 120 LPs
0089c_MA120 2014, MSc Informatik (Mono), 120 LPs
0207b_m37 2015, MSc Informatik (Lehramt), 37 LPs
0208b_m42 2015, MSc Informatik (Lehramt), 42 LPs
0458a_m37 2015, MSc Informatik (Lehramt), 37 LPs
0471a_m42 2015, MSc Informatik (Lehramt), 42 LPs
0556a_m37 2018, M-Ed Fach 1 Informatik (Lehramt an Integrierten Sekundarschulen und Gymnasien), 37 LPs
0557a_m42 2018, M-Ed Fach 2 Informatik (Lehramt an Integrierten Sekundarschulen und Gymnasien), 42 LPs
0590a_MA120 2019, MSc Data Science, 120 LP
0590b_MA120 2021, MSc Data Science, 120 LP

Human Centered Data Science S22
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Main Events

Day Time Location Details
Thursday 16-18 T9/055 Seminarraum 2022-04-21 - 2022-07-21
Thursday 16-18 T9/SR 005 Übungsraum 2022-05-05 - 2022-07-21

Accompanying Events

Day Time Location Details
Tuesday 10-12 T9/051 Seminarraum Übung 01

Human Centered Data Science S22
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