UNIGE Data Science Day

The UNIGE Data Science Day is a scientific symposium for researchers at the UNIGE, which takes place at the beginning of the academic year.

Each year, the CCSD stimulates a collective reflection on a particular theme. This reflection is introduced during the UNIGE Data Science Day and continued in the framework of other activities launched by the Center throughout the year. This theme is intended to be precise enough to encourage a significant scientific contribution and a rich dialogue, but also transversal enough to allow for interdisciplinarity.

For its 2023 edition, the theme of Data Science Day is "Data Science for All : How to teach and integrate Data Science in diverse Disciplines ".

This meeting will take place on September 14, 2023 from 9:15 am to 6:30 pm.


More information on the programme below.


Interested in taking part of the Data Science Day 2023?

Register here >>

(registrations open until the 4th of September 2023)

2023 Theme: Data Science for all

Data science has become an essential tool for organizations and individuals to extract insights and value from vast amounts of data. However, despite the growing importance of data science, there are still significant challenges in teaching and integrating it in diverse disciplines. While some fields, such as engineering and computer science, have long embraced quantitative methods and data analysis, many other academic disciplines, such as the humanities and social sciences, have not traditionally emphasized these skills. This can result in a significant gap in data literacy across disciplines, which in turn can hinder interdisciplinary collaborations and limit the potential benefits of data science.

To address these challenges, this conference aims to bring together experts from diverse fields to discuss how data science can be taught and integrated into different disciplines, with a particular focus on the research dimension. By sharing best practices and innovative approaches to teaching data science, we hope to promote the democratization of data science education and enable individuals from all backgrounds and disciplines to become data-literate and able to use data to inform research and decision-making.

We will host communications that address topics such as:

  • Innovative approaches to teaching data science, such as interdisciplinary courses and experiential learning opportunities, that enable students to develop the skills they need to conduct data-driven research
  • Best practices for integrating data science into different disciplines, including strategies for overcoming resistance or skepticism towards data science in some fields and for promoting interdisciplinary collaborations between data scientists and researchers from other fields
  • Challenges and solutions for teaching data science to diverse student populations, and for addressing disparities in data literacy across disciplines
  • Use cases of data science applications in various fields, showcasing the potential benefits of data science for different disciplines and highlighting successful interdisciplinary collaborations
  • Opportunities and challenges for interdisciplinary collaborations in data science education, such as building partnerships across departments or institutions and navigating differences in terminology and methods, and for promoting research that integrates data science with other fields

detailed programme


9h15-9h30: Introduction (room MR280)

- Guive Khan Mohammad, Digital Transformation Office, "UNIGE Data Science Competence Center"


9h30-12h00: Plenary Session "Data Science for All : How to teach and integrate Data Science in diverse Disciplines" (room MR280)

- Jean-Luc Falcone, Faculty of Sciences, "New Bachelor of Computational Sciences (BaSC) with areas of application"

- Olivier Renaud, Faculty of Psychology and Educational Sciences, "Is it important to use examples specific to the learner's field ? Experience in teaching to psychologists"

- Michael Dayan, FCBG Human Neuroscience Platform, "From the ground up to Jupyter: learning from scratch a data science tech stack including computational notebooks"

- Douglas Teodoro and Alban Bornet, Faculty of Medicine, "Extension of data science-related courses at the Faculty of Medicine: Machine Learning Applied to Healthcare and Introduction to Computational Medicine"

- Claire Balleys and Tommaso Venturini, Geneva School of Social Sciences, "Studying digital cultures from a social science perspective. Presentation of the new Masters in Communication and Digital Cultures"

- Gregory Giuliani, Institute for Environmental Sciences, "Earth Observations Data Science for All - Reverse and Self learning methods"

- Béatrice Joyeux-Prunel, Faculty of Humanities, "TBD"

- Matthew Leigh and Samuel Klein, Faculty of Sciences, "AI in RODEM-ATLAS & AI in physics — teaching modern tools at the University of Geneva"

- Paola Merlo, Faculty of Humanities, "Marrying computer science and the humanities: the curious case of computational linguistics"

- Sylvain Sardy, Faculty of Sciences, "Statistical machine learning for pharmacologists, biologists, earth scientists and mathematicians"

- Giovanna Di Marzo Serugendo, Geneva School of Social Sciences, Computer Science Center, "Learning through Digital Innovation and Interdisciplinarity"

- Jean-Blaise Claivaz, Division de l'information scientifique, "Research Data: cross-disciplinary support for research data management"

- Coralie Fournier, Faculty of Medicine, "Using digital technology to manage scientific data in the Faculty of Medicine"

- Matthias Studer, Geneva School of Social Sciences, "Randomized Moodle exercises for quantitative methods courses"

- Anastasia Floru, Geneva School of Economics and Management, "Why not learning with fun ? Escape Game an innovative tool to test your skills"

- Sara Botera Mesa, Faculty of Medicine, "The GRAPH Courses: data science training platform for the modern data analyst"

- Mina Bjelogrlic, Faculty of Medicine, "Leveraging digital understanding and skills in medical data sciences"

- Jean-Luc Falcone, Faculty of Sciences, "Scientific Computing Support"

- Julien Prados, Faculty of Medicine, "Learning Data-Science with the Bioinformatics Support Platform for data analysis"

- Raphaël Thézé, Bureau de la transformation numérique, "P3: project-based learning and data science"

- Patrick Roth, Division Système et Technologies de l'Information et de la Communication, "Take Over: the UNIGE peer-based learning program to improve digital skills"


12h00-13h30: lunch (Hall of Uni Mail)


13h30-15h00: rounds tables

Organised by Hugues Cazeaux (DiSTIC) and Guive Khan-Mohammad (BTN)


Hugues Cazeaux (DiSTIC-eResearch), Lydie Echernier (DIS-UNIGE Data Stewardship Action Plan), Julien Prados (Faculty of Medicine-Bioinformatics Support Platform for Data Analysis), Yann Sagon (DiSTIC-HPC),


In today's fast-paced digital era, data science has emerged as a powerful force driving cutting-edge research. However, the infrastructures and services supporting data science practices often face limitations in adapting to the rapidly evolving landscape. To overcome these challenges and foster a culture of innovation, it is crucial to explore and adopt innovative ways of thinking. In this dynamic round table, we aim to provide a comprehensive overview of the data science infrastructures and services at the University of Geneva. We will explore the institutional solutions put in place to support data-driven research and collaboration across various disciplines. Additionally, we will shine a spotlight on the decentralized innovative initiatives that have emerged organically within our institution, showcasing the diversity of approaches and the potential for groundbreaking advancements. Join us in this forward-thinking round table to gain valuable insights, exchange ideas, contribute to the ongoing dialogue on transforming data science infrastructures and services, and co-construct tomorrow's collaborationand synergies in our institution.

Organised by Lucia Gomez Teijeiro (Behavorial Philanthropy Lab) and Guive Khan Mohammad (Bureau de la Transformation Numérique)


Natalia Baumann (Basic Neurosciences), Lamia Friha (Cellule R&D, DiSTIC), Mattia Fritz (Faculty of Psychology and Educational Sciences-TECFA), Lucia Gomez Teijeiro (Behavorial Philanthropy Lab) and Tommaso Venturini (Geneva School of Social Sciences).


Join a panel of expert speakers for an immersive round-table discussion on empowering inclusive data science education by breaking psychological barriers. This interactive session will delve into the key factors that hinder or foster the adoption of data science in research and education while emphasizing its integration into traditionally non-data-driven fields. Prepare to be inspired as we share experiences and approaches to bridge the data science gap, emphasizing transformative applications in diverse domains, demonstrating innovative ways to comprehensively display complex concepts, and delving into the mindset transition needed to master computational thinking. Within this interdisciplinary environment, we will highlight the significance of empowering students in both learning and teaching data science: a supportive and collaborative peer-based community is key for nurturing success, elevating the visibility of data education, and shaping its future. Be part of this pivotal conversation aiming at driving a positive change, fostering inclusivity, and unlocking the immense possibilities that data science offers for everyone.


15h30-17h00: Workshops

Organised by Lamia Friha (DiSTIC-R&D)


Lamia Friha, Alain Hugentobler, Paul Mulard, Antonin Akouété Sedoh


In today's data-driven world, the rapid and exponential generation of data has become a defining feature of various fields. To make sense of this vast amount of information, the use of visualization techniques and tools has become absolutely essential. Using these visual aids, analysts and decision-makers can effectively navigate complex data sets, identify potential correlations and uncover underlying trends that might otherwise remain hidden in the data. In this workshop, we aim to present such innovative visualization methods that enable information to be represented in new ways, illustrated by practical use cases.
The workshop will begin with a definition and overview of data visualization methods and tools, followed by a brief description of the overall approach. Then, specific projects will be addressed, followed by an interactive discussion with all participants to enable them to project themselves into this new field.

Organised by Bokar Lamine N'Diaye (Faculty of Humanities)


While state-of-the-art generative tools drawing on machine learning to generate text or visual content keep getting larger and more widespread, their interfaces and training data - designed for monetization and a certain view of productivity - may hinder or constrict the creative process of their users. This constitutes a strong incentive for what the generative artist Tyler Hobbs designates as "long form generative art": a conscious and curated approach to the generative process itself. This workshop will present a sample of methods allowing for artistic practitioners (digital or not) to reflect on the significance and artistic charge of the generative process. The work of Emily Short, rewowned author in the field of interactive fiction and procedural worldbuilding, will serve as a basic framework for the participants to apply these methods to generate different outputs (textual and visual) in the form of individual cards, forming a collectively created Tarot deck. (Participants are welcome to familiarize themselves with this Google Drive folder, where a number of tools and references used during the workshop will be progressively uploaded.)


17h00-18h30: Apéritif (Hall of Uni Mail)

previous editions


More information on the 2021 edition >>

More information on the 2020 edition >>