Enabling Innovation with Data Science
This description is only available in English.
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17.01–28.02.2025
Course date
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5 days
Course duration
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Zurich
Course location
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English
Course language -
31.12.2024
Registration deadline
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CHF 4000
Course fee
Course description
This course focuses on achieving impact and innovation with data science. It features theoretical lectures on selected applications of data science, practical lectures on leveraging data science within a business context, and a selection of exemplary use cases from the private and public sector.
Date and venue
Course date
17.01–28.02.2025, 5 days
Course location
SDSC, Andreasturm, Andreasstrasse 5, 8092 Zurich
Fees
Course fee
CHF 4000
CHF 3600 for ETH Zurich employees
CHF 3600 for ETH Zurich alumnae and alumni
CHF 3600 for employees from SDSC partners
General terms and conditions (GTC)
Organiser
Course management
Dr Anna Fournier, Principal Data Scientist, SDSC – ETH Zurich
Lecturers:
- Professor Olivier Verscheure, Executive Director, Swiss Data Science Center (SDSC) – EPFL & ETH Zurich
- Dr Silvia Quarteroni, Head of the Innovation Unit, SDSC – EPFL & ETH Zurich
- Dr Anna Fournier, Principal Data Scientist, SDSC – ETH Zurich
- Dr Matthias Galipaud, Sr Data Scientist, SDSC – ETH Zurich
- Dr Dan Assouline, Sr Data Scientist, SDSC – EPFL
- Dr Saurabh Bhargava, Principal Data Scientist, SDSC – ETH Zurich
- Lucas Chizzali, Sr Data Scientist, SDSC – ETH Zurich
- Christian Schneebeli, Sr Data Scientist, SDSC – ETH Zurich
- Arshjot Khehra, Data Scientist, SDSC – ETH Zurich
- Dr Valerio Rossetti, Principal Data Scientist, SDSC – EPFL
- Thibaut Loiseau, Machine Learning Engineer, SDSC - EPFL
- Dr Alessandro Nesti, Principal Data Scientist, SDSC - EPFL
Contact
Wasserwerkstrasse 10/12
WWA E 13
8092
Zurich
Responsible body
Swiss Data Science Center – a joint venture of ETH Zürich and EPFL
Good to know
Target group
This course addresses experienced professionals up to executive levels. Participants should have prior experience working with data in an applied context (e.g. reporting, visualisation, summary statistics). No programming skills nor technical knowledge on data science specifically are required.
Course language(s)
English
Additional information
Day 1 : Intro to data science and digital transformation
- History, terminology, basic concepts, project management
- Legal and ethical aspects of AI
- Hands-on session with no-code platform (KNIME)
Day 2 : Fundamentals of machine learning
- Supervised learning, with hands-on
- Best practices for industrialisation of solutions and reusability of digital assets
- Use cases
Day 3 : Fundamentals of machine learning
- Unsupervised learning and time series, with hands-on
- Canvassing exercise
- Fostering adoption: model explainability and AB testing
Day 4 : Natural Language Processing and generative AI
- Algorithms and applications, with hands-on
- Prompt engineering workshop
- Use cases
Day 5 AM: Computer Vision
- Algorithms and applications
- Use cases
- Canvas presentation and discussion