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DAS ETH in Data Science

This description is only available in English.

  • Autumn and Spring Semester

    Start

  • 1 year

    Duration
  • ETH Zurich
    Location
  • English
    Tuition language

  • 01–31.10.2024
    01–30.04.2025

    Application period

  • CHF 12,000

    Programme fee

In brief: The DAS ETH in Data Science

The DAS ETH in Data Science is a one-year, part-time programme for professionals to strengthen their skills in the management, analysis and utilisation of complex data sets in courses and a final project.

Structure and content

Programme description

The interdisciplinary programme conveys knowledge across the fields of mathematics, computer science and electrical engineering. This includes all levels of abstraction of technologies relevant to data science, from hardware and electronics, clusters and networks, through big data systems, to machine learning, algorithms and statistics. It also offers insights into political, societal, legal, ethical and privacy aspects of data science.

Objectives

The participants are taught how to understand and use complex data management (storage, querying, infrastructures, networks etc.) and analysis techniques (machine learning, statistics etc.) in order to utilise them in a broad range of applications.

Professional perspectives

Given the rapid growth of data and the need to analyse it, there is a critical skill shortage in the area of data science. Almost all sectors, including the public sector, need data scientists. Besides technical specialists, also executive staff members need a basic understanding of data science problems and opportunities for their organisations.

Structure and format

To complete the programme, a total amount of 35 to 45 ECTS has to be obtained, split over a foundations course (6 to 8 ECTS), a specialisation track (at least 12 ECTS), further courses to choose from a list, and a capstone project (8 ECTS).

Additional information

The DAS programme contains foundations courses, flexibility of specialisation in advanced topics and a capstone project, which provides hands-​on experience.

The following specialisation tracks are currently offered:

  • Hardware for Machine Learning
  • Image Analysis and Computer Vision
  • Neural Information Processing
  • Statistics
  • Machine Learning and Artificial Intelligence
  • Big Data Systems

The students must apply for a starting semester in which the chosen foundations course is offered.

Limited number of participants: Admission is based on programme-​specific admissions criteria and/or the constitution of the respective group of applicants.

Tuition language(s)

English

Credits

35–45 ECTS credits
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Target group and admission

Target group

Professionals with a strong background in computer science or mathematics who wish to obtain an in-depth knowledge in data science. This includes engineers and executive staff members from the industry or the public sector who need in-depth knowledge in data science.

Requirements

Master's degree acknowledged by ETH in computer science, data science, mathematics, statistics, physics, mechanical engineering, electrical engineering or in a related field or equivalent educational qualifications; existing work experience.

Required language skills

English: C1 – external page Show proficiency scales

Language(s) of performance assessment

English
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Dates and venue

Start

Every Autumn and Spring Semester

Duration

1 year, part-time (1050 hours. The workload can be spread, on request, on up to two years.)

Application period

01–31.10.2024 for Spring Semester 2025 (admission after the application deadline)
01–30.04.2025 for Autumn Semester 2025 (admission after the application deadline)

Location

ETH Zurich
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Fees

Programme fee

CHF 12,000

Application fee

CHF 50 for persons with a Swiss university degree, CHF 150 for persons with another university degree (non-​refundable, credit card payment only)

Withdrawal fee

  • Within 30 days after the admission date: free of charge 
  • More than 30 days after the admission date: CHF 4,000  
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Application

Questions about the application

ETH Zurich, School for Continuing Education, HG E 17–​18.5, Rämistrasse 101, 8092 Zurich, Tel. external page +41 44 632 56 59, E-mail

Recommended procedure for a successful application

Application documents

Additional application documents

1) complete Download study plan (DOCX, 50 KB) proposal that indicates the specialisation track. Ideally, a study plan should be designed over 2 semesters in order to keep a buffer of 2 more semesters in case of having to repeat an exam. A study plan over 3 semesters is also possible; exceptions beyond 3 semesters can only be made on request, in which case a motivation regarding this must be provided with your application.

2) fill all the information on the admissions form; in particular, be sure to write between 150 and 250 words regarding your motivation to attend this programme, as we are interested in learning more about your background and goals.

3) submit a resume

Optionally, you can also

4) submit references or reference letters

5) submit a language certificate (C1)

Incomplete applications may be rejected or processed at our own discretion. We reserve ourselves the right to postpone an application to the next possible semester if the chosen foundations course is not offered in the semester applied for.

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Organiser

Programme management

Professor Gunnar Rätsch
Programme Co-Director
Professor Gunnar Rätsch
Professor Nicolai Meinshausen
Programme Co-Director
Professor Nicolai Meinshausen
Professor Helmut Bölcskei
Programme Co-Director
Professor Helmut Bölcskei
Dr Ghislain Fourny
Programme Manager
Dr Ghislain Fourny

Contact

Dr Ghislain Fourny
Programme Manager

ETH Zurich
Stampfenbachstrasse 114
STF H 311
8092 Zurich

Tamlyn Altmann
Programme Administration

ETH Zurich
Universitätstrasse 6
CAB F 64.1
8092 Zurich

Responsible body

ETH Zurich, Department of Computer Science (D-INFK), Department of Mathematics (D-MATH), Department of Information Technology and Electrical Engineering (D-ITET), Swiss Data Science Center

Further information

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