Jan 17, 2025
Feb 28, 2025

ENID | Enabling Innovation with Data Science at ETH Zurich

Harness data to improve decisions and processes within your organization
9:00h-17:00h
ETH Zurich "Swiss AI Tower" - Andreasturm, Andreasstrasse 5, 8092 Zurich-Oerlikon, Switzerland
Courses & Workshops
Speaker(s):

Presentation

In today’s world, data is everywhere, yet the ability to harness its potential for informed decision-making, and consequently, its significant impact on business, remains elusive to most organizations. But how should an organization start to become data-driven? What are the best practices for implementing data science models that benefit the entire organization? And how to negotiate executive buy-in for data science initiatives?

This 5-day course focuses on achieving impact and innovation with data science. It features theoretical lectures on selected applications of data science, e.g. natural language processing and computer vision, and practical lectures on leveraging data science within a business context, such as project management, impact evaluation, performance metrics, stakeholder management.

Details

Target Audience

Experienced professionals and executives wishing to steer data science initiatives and generate business impact. The course will be given in English.

Objectives

  • Understand the foundational principles and techniques of data science within the broader context of artificial intelligence (AI) and machine learning, including deep learning applications
  • Be able to objectively assess complexity and scalability of AI use cases
  • Acquire the tools to manage AI projects from scoping to Minimum Viable Product (MVP) solution deployment
  • Explore real-world applications through hands-on machine learning assignments and discover concrete data science applications
  • Connect and share with other industry professionals

Instructors

  • Prof. Olivier Verscheure, Executive Director, Swiss Data Science Center (SDSC) - EPFL & ETH Zurich
  • Dr. Silvia Quarteroni, Head of Innovation, SDSC - EPFL & ETH Zurich
  • Dr. Anna Fournier, Principal Data Scientist, SDSC - ETH Zurich
  • Dr. Matthias Galipaud, Senior Data Scientist, SDSC - ETH Zurich
  • Dr. Dan Assouline, Senior Data Scientist, SDSC - EPFL
  • Dr. Saurabh Bhargava, Principal Data Scientist, SDSC - ETH Zurich
  • Lucas Chizzali, Senior Data Scientist, SDSC - ETH Zurich
  • Christian Schneebeli, Senior 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

Certification

A certificate of attendance will be delivered at the end of the course.

Organisation

The Swiss Data Science Center (SDSC) is a strategic focus area of the ETH domain, with data professionals located at Ecole Polytechnique Fédérale de Lausanne (EPFL), the Eidgenössische Technische Hochschule Zürich (ETH) and Paul Scherrer Institut (PSI).

For any other course-related questions, please contact Dr. Anna Fournier at anna.fournier@sdsc.ethz.ch

Program Director

Prof. Olivier Verscheure, Executive Director, Swiss Data Science Center (SDSC)

Dates and schedule

This 5 day course will take place at the following dates:

  • January 17, 2025
  • January 24, 2025
  • January 31, 2025
  • February 7th, 2025
  • February 28th, 2025

Course venue

ETH Zurich "Swiss AI Tower" - Andreasturm, Andreasstrasse 5, 8092 Zurich-Oerlikon, Switzerland
Room 15 at the 14th floor

Required Material

Participants should bring their own laptop (installation of KNIME Analytics Platform free software is necessary for hands-on experience)

Course fee

4000.- Swiss Francs
10% special discount for ETH employees and alumni, as well as SDSC partners

Registration deadline

December 31st, 2024.
The number of participants is limited.

Registration

Programme

DAY 1 : INTRODUCTION TO DATA SCIENCE AND DIGITAL TRANSFORMATION

  • Data science history, terminology and basic concepts, overview of learning tasks
  • Digital transformation – becoming data-driven, project management strategies and tools
  • Hands-on session with no-code platform (KNIME) – supervised learning
  • Legal and ethical aspects of AI

DAY 2 : FUNDAMENTALS OF MACHINE LEARNING (PART 1)

  • Strength and limitations of different supervised learning algorithms (including deep learning) and performance metrics, with hands-on session (KNIME)
  • Best practices for industrialisation of solutions and reusability of digital assets
  • Presentation of use cases delivered by SDSC

DAY 3 : FUNDAMENTALS OF MACHINE LEARNING (PART 2)

  • Strength and limitations of different algorithms for unsupervised learning and time series forecasting, with hands-on session (KNIME)
  • Canvassing exercise – how to start a project on the right track
  • Fostering adoption: model explainability techniques, AB testing for business impact assessment

DAY 4 : NATURAL LANGUAGE PROCESSING (NLP) AND GENERATIVE AI

  • History of NLP, algorithms and applications, with hands-on session (KNIME and ChatGPT)
  • Generative AI in NLP and other areas. State-of-the-art LLMs, such as GPT-4
  • Prompt engineering workshop
  • Presentation of use cases delivered by SDSC
  • Group discussion and feedback on canvassed projects by participants

DAY 5 : COMPUTER VISION

  • Group discussion and feedback on canvassed projects by participants
  • Computer Vision (CV) algorithms and applications
  • Presentation of use cases delivered by SDSC
  • Feedback and course closing

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