Enabling Innovation with Data Science
This description is only available in English.
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07.06–05.07.2024
Course date
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5 days
Course duration
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ETH Zurich
Course location
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English
Course language -
17.05.2024
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
07.06–05.07.2024, 5 days
Course location
Online
Fees
Course fee
CHF 4000
CHF 3600 for ETH Zurich alumnae and alumni
CHF 3600 for 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 Dan Assouline, Sr Data Scientist, SDSC - EPFL
- Dr Saurabh Bhargava, Principal Data Scientist, SDSC – ETH Zurich
- Dr Roberto Castello, Principal Data Scientist, SDSC - EPFL
- Lucas Chizzali, Sr Data Scientist, SDSC – ETH Zurich
- Dr Anna Fournier, Principal Data Scientist, SDSC – ETH Zurich
- Dr Matthias Galipaud, Sr Data Scientist, SDSC – ETH Zurich
- Dr Olivier Kauffmann, Data Scientist, SDSC - EPFL
- Clément Lefebvre, Sr Data Scientist, SDSC - EPFL
- Thibaut Loiseau, Machine Learning Engineer, SDSC - EPFL
- Dr Alessandro Nesti, Principal Data Scientist, SDSC - EPFL
- Dr Silvia Quarteroni, Head of the Innovation Unit, SDSC - EPFL & ETH Zurich
- Dr Valerio Rossetti, Principal Data Scientist, SDSC - EPFL
- Christian Schneebeli, Sr Data Scientist, SDSC – ETH Zurich
- Victor Van Wymeersch, Data Scientist, SDSC – ETH Zurich
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 (eg reporting/ visualization/ 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
- 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
- Fostering adoption: model explainability and AB testing
- Canvassing exercise
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, with hands-on
- Use cases