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Root number
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493415 |
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Semester
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FS2025 |
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Type of course
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Lecture |
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Allocation to subject
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Psychology |
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Type of exam
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Written exam |
| Title |
Lecture series: Transdisciplinary Data Science to Understand Human Beings in a Digitalised World |
| Description |
Digitalisation, and the information age which it has fostered, have fundamentally altered the day-to-day practices of academic researchers. Almost without exception, every field has embraced modern methods from the broad new field known as data science. Historians may now analyse and synthesise swatches of historical texts in the blink of an eye; psychologists can examine the real-time behaviour of thousands of individuals simultaneously and extract elaborate patterns of their behaviour; economists may simulate and manipulate the dynamics of whole economic systems; climate scientists can incorporate real-time, granular data to predict climatic patterns in very specific regions of the world.
Despite the ubiquity of data science practices across these disciplines, the specific methods which are employed (and the reasons for which they are employed) can vary drastically, and methods which are employed widely in one field can be rather uncommon, or even ignored altogether, by another field. The goal of this lecture series is to facilitate the transdisciplinary use of data science methods and foster conversations across disciplines regarding the theory and practice of data science in the academy.
The lecture series is structured in three sections: The first section deals with the conceptual and theoretical background of data science in digitalised contexts, as well as emergent issues relating data workflow management and robust practices using these methods. The second section aims to provide informative introductions to various methods within data science which may be generally applied across a variety of fields, but which may be better known to some than to others. Finally, the third section deals with infrastructural issues relating to the use of data science methods, as well as contemporary considerations in the wake of cutting-edge breakthroughs in machine learning.
This lecture series is aimed at researchers from all academic disciplines, and lecturers are encouraged to make content jargon-free and accessible, using general and broad examples wherever possible. Each lecture should act as a jumping-off point on the relevant topic which can encourage attendees to explore topics of interest further.
Bachelor students are very welcome! However, the course can only be credited in the Master's program. |
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ILIAS-Link (Learning resource for course)
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Registrations are transmitted from CTS to ILIAS (no admission in ILIAS possible).
ILIAS
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Link to another web site
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| Lecturers |
Prof. Dr.
Malte Elson, Institute of Psychology ✉
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Dr.
Jamie Tane Cummins, Institute of Marketing and Management, Consumer Behavior ✉
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ECTS
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3 |
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Recognition as optional course possible
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Yes |
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Grading
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1 to 6 |
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| Dates |
Thursday 16:15-18:00 Weekly
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| Rooms |
Hörsaal 003, Hörsaalgebäude vonRoll
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| Students please consult the detailed view for complete information on dates, rooms and planned podcasts. |
User:
Öffentlicher Bereich
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