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Curriculum Data Science

The curriculum of your degree consists of different module types, which provide you with specialist knowledge, mathematical-scientific basics, practical experience as well as contextual knowledge from areas such as communication and business.

In the third year of study, depending on your interest, you can choose between three main areas and take the corresponding specialization and elective modules. Click on each module to learn more about choices, language of instruction and ECTS credits.

Module overview full-time

This module table is valid from 15. July 2020

Module legend

Context Modules

Project Modules

Subject-Specific Modules

Mathematics and Nat. Science Modules

Semester 1 , Semester ECTS : 30 , Semester week lessons : 30

Communication Competence 1

Communication Competence 1

The Communication Competence 1 module focuses on the following aspects of communication training: Researching and processing information in a scientific context // Developing presentation skills // Organising communication and feedback in teams // Teaching language: DE/EN

Software Projects

Software Projects

In this first project module, the knowledge from other modules is applied and enriched with initial experiences of project work in a team. This includes requirements specification, software design, technical writing, teamwork, project planning and code management. // Teaching language: DE

Computer Science Programming 1

Computer Science Programming 1

Introduction to software development with Python. // Teaching language: DE

Databases

Databases

Basics of relational databases: relational algebra, entity-relationship design, SQL DDL/DML, efficient and correct queries in SQL, indexes, triggers, transactions/ACID // Teaching language: DE

Data Science Fundamentals

Data Science Fundamentals

The course provides an introduction into the fundamental aspects of the data science practice. The students develop an understanding for the technical, ethical and legal challenges in the development of data products. The concepts are implemented in practical use cases. // Teaching language: DE

Explorative Data Analysis

Explorative Data Analysis

The module Exploratory Data Analysis introduces the basics of descriptive statistics. In this module, the students learn to perform descriptive data analyses, which includes preparing, visualizing and describing the data with key figures using the statistical software R. // Teaching language: DE

Linear Algebra 1

Linear Algebra 1

Students are familiarised with and master the basic concepts and propositions of linear algebra and analytic geometry. They can formulate simple concrete questions in the mathematical language and are able to solve these independently and present their solutions. // Teaching language: DE

Analysis 1

Analysis 1

In this course, students learn the basic concepts of calculus of one real variable. // Teaching language: DE

Semester 2 , Semester ECTS : 30 , Semester week lessons : 30

Communication Competence 2

Communication Competence 2

The Communication Competence 2 module focuses on the following aspects of communication training: Collaborative writing and peer feedback in a scientific/professional context // Developing research skills // Audience-oriented communication // Teaching language: DE/EN

Data Processing with R

Data Processing with R

Preparing, cleansing and visualising data are central tasks of a data scientist. In this module students train and consolidate the necessary skills in project teams, which they have acquired in the modules Explorative Datenanalyse and Datenbanken. // Teaching language: DE

Computer Science Programming 2

Computer Science Programming 2

Students’ existing programming skills are enhanced and applied on a practical level. The module conveys the competences for developing robust software applications in ubiquitous environments. Modelling approaches, failure sources and optimisation opportunities are understood at a code level. // Teaching language: DE

Data Engineering 1

Data Engineering 1

The field of "Data Engineering" covers the crucial steps from acquisition of the raw data to making the validated, cleaned data available for exploitation. The "Data Engineering 1" module discusses the basics of this field and the handling of unstructured data. // Teaching language: DE

Visualisation and Data Science Storytelling

Visualisation and Data Science Storytelling

Students acquire basic knowledge of data visualisation and data science storytelling. The course includes visual elements, functions and effects, plus analysis and interpretation of data visualisations. In practical exercises, students learn how to communicate effectively with data visualisations. // Teaching language: DE

Probability Calculations

Probability Calculations

The module Probability Theory introduces the basics of probability theory. In this module, students learn to describe random events and their properties with probability models and to quantify them with the statistical software R. // Teaching language: DE

Linear Algebra 2

Linear Algebra 2

Students are familiarised with and master the basic concepts and propositions of linear algebra and complex numbers. They can formulate simple concrete questions in the mathematical language and are able to solve these independently. // Teaching language: DE

Analysis 2

Analysis 2

Basic concepts and methods of differential and integral calculus of one real variable, as well as their application. // Teaching language: DE

Semester 3 , Semester ECTS : 30 , Semester week lessons : 30

Communication Competence 3

Communication Competence 3

The Communication Competence 3 module focuses on the following aspects of communication training: Communication in international and interdisciplinary settings // Mediation and transfer between English and German // Negotiation and discussion // Teaching language: DE/EN

Text Processing

Text Processing

In this module, three small projects are carried out in the field of automatic text processing. In the first two projects, students familiarise themselves with the topic and methodology, in the third they implement a project independently. // Teaching language: DE

Operating Systems and Infrastructure

Operating Systems and Infrastructure

Efficient use of data and computationally-intensive applications requires basic operating system concepts to be understood. Students use remote virtualised infrastructure and services for data processing, creating and linking cloud services to run data- or computationally-intensive applications. // Teaching language: DE

Algorithms and Data Structures

Algorithms and Data Structures

Students cover basic algorithms and data structures of computer science. They learn to solve algorithmically complex tasks, know the central data structures of computer science and how they are implemented in Python, and can estimate the computational effort of programs and apply basic design patterns. // Teaching language: DE

Data Products and Services

Data Products and Services

The DPS module covers the basics of process management and business operations in relation to service management and the documentation and communication of services. // Teaching language: DE

Basics of Statistics

Basics of Statistics

Basics of statistics introduces students to the fundamentals of statistical inference, i.e. techniques that allow to draw inferences about a population from a sample. Special emphasis is put on computational methods that allow for the theoretical concepts to be applied in practice. // Teaching language: DE

Analysis 3

Analysis 3

In this module, students learn about linear ordinary differential equations and systems of first-order ODEs. In addition, the basic properties and calculus of functions of several variables are discussed. // Teaching language: DE

Electronics and Digital Technology: Basic Principles

Electronics and Digital Technology: Basic Principles

The physical principles of sensors are discussed, taking examples. Based on the laws of physics, the processes of measurement, the processing of raw data and the relationship of this data to data-based models, are explored both theoretically and experimentally. // Teaching language: DE

Semester 4 , Semester ECTS : 30 , Semester week lessons : 30

Digitisation of Economic Systems

Digitisation of Economic Systems

Digitalisation is profoundly changing our entire economic system. This lecture examines the concrete effects of digital transformation on the business models of established and new companies. The opportunities arising from digitisation and the obstacles or risks that exist are examined. // Teaching language: DE

Big Data Project

Big Data Project

Students gain practical experience of working with Big Data problems. Based on the theoretical foundations of “Data Engineering 1” and “Data Engineering 2”, students analyse selected topics from these foundation courses and implement scalable applications using the latest Big Data technologies. // Teaching language: DE/EN

Front End, Web and Software Engineering

Front End, Web and Software Engineering

Students learn different frontend development technologies, starting with an introduction to GUI development in Python with PyQt and Matplotlib. Afterwards, the focus is on the development of web pages for showing charts, employing technologies like HTML, CSS and JavaScript. // Teaching language: DE

Data Engineering 2

Data Engineering 2

Data Engineering topics are essential components of successful data products and data projects. Students learn the requirements for running successful data engineering pipelines, the key methods, and both the theoretical foundations and practical implementation of different methods and applications. // Teaching language: DE

Digital Entrepreneurship

Digital Entrepreneurship

Students learn basic models and methods for achieving entrepreneurial success with Smart Connected Products and digital services. Alongside this, the acquired knowledge is implemented by way of example with start-up companies and also in innovative projects in established companies. // Teaching language: DE

Machine Learning und Data Mining

Machine Learning und Data Mining

Machine learning and data mining are essential components of successful data products and projects. Students are familiarized with the prerequisites for their use and with various methods for different applications. They study the theoretical fundamentals and the implementation of the methods. // Teaching language: DE

Statistical Modelling

Statistical Modelling

The module introduces students to the basics of statistical modelling using linear regression analysis. Aspects of the model structure, inference, prediction, residuals analysis and model building, including variable selection, are examined in detail, both theoretically and in case studies. // Teaching language: DE

Numerics

Numerics

This class gives an introduction to the theory and algorithms of numerical mathematics. // Teaching language: DE

Semester 5 , Semester ECTS : 30 , Semester week lessons : 30

Elective Module

Elective Module

Elective Module

Elective Module

Project Thesis: Data Science

Project Thesis: Data Science

Students work independently, typically in teams of two, on a concrete scientific/technical question under the guidance of a lecturer. The topic of the project work should come from the field of data science. The written final report includes the project implementation and the results obtained. // Teaching language: DE

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Optional Module

Optional Module

  • ECTS : 4

Module from group

You choose a elective module based on your interests.

Semester 6 , Semester ECTS : 30 , Semester week lessons : 30

Elective Module

Elective Module

Bachelor Thesis: Data Science

Bachelor Thesis: Data Science

Students work independently on a concrete scientific/technical question under the guidance of a lecturer. The topic of the project work should come from the field of data science. The Bachelor thesis is typically prepared by a team of two students, with a written final report. // Teaching language: DE

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

The list of elective modules reflects the current state of the offer. It can change until the third year of study.

Module overview part-time

This module table is valid from 15. July 2020

Module legend

Context Modules

Subject-Specific Modules

Mathematics and Nat. Science Modules

Project Modules

Semester 1 , Semester ECTS : 22 , Semester week lessons : 18

Communication Competence 1

Communication Competence 1

The Communication Competence 1 module focuses on the following aspects of communication training: Researching and processing information in a scientific context // Developing presentation skills // Organising communication and feedback in teams // Teaching language: DE/EN

Computer Science Programming 1

Computer Science Programming 1

Introduction to software development with Python. // Teaching language: DE

Databases

Databases

Basics of relational databases: relational algebra, entity-relationship design, SQL DDL/DML, efficient and correct queries in SQL, indexes, triggers, transactions/ACID // Teaching language: DE

Data Science Fundamentals

Data Science Fundamentals

The course provides an introduction into the fundamental aspects of the data science practice. The students develop an understanding for the technical, ethical and legal challenges in the development of data products. The concepts are implemented in practical use cases. // Teaching language: DE

Linear Algebra 1

Linear Algebra 1

Students are familiarised with and master the basic concepts and propositions of linear algebra and analytic geometry. They can formulate simple concrete questions in the mathematical language and are able to solve these independently and present their solutions. // Teaching language: DE

Analysis 1

Analysis 1

In this course, students learn the basic concepts of calculus of one real variable. // Teaching language: DE

Semester 2 , Semester ECTS : 22 , Semester week lessons : 22

Communication Competence 2

Communication Competence 2

The Communication Competence 2 module focuses on the following aspects of communication training: Collaborative writing and peer feedback in a scientific/professional context // Developing research skills // Audience-oriented communication // Teaching language: DE/EN

Computer Science Programming 2

Computer Science Programming 2

Students’ existing programming skills are enhanced and applied on a practical level. The module conveys the competences for developing robust software applications in ubiquitous environments. Modelling approaches, failure sources and optimisation opportunities are understood at a code level. // Teaching language: DE

Data Engineering 1

Data Engineering 1

The field of "Data Engineering" covers the crucial steps from acquisition of the raw data to making the validated, cleaned data available for exploitation. The "Data Engineering 1" module discusses the basics of this field and the handling of unstructured data. // Teaching language: DE

Visualisation and Data Science Storytelling

Visualisation and Data Science Storytelling

Students acquire basic knowledge of data visualisation and data science storytelling. The course includes visual elements, functions and effects, plus analysis and interpretation of data visualisations. In practical exercises, students learn how to communicate effectively with data visualisations. // Teaching language: DE

Linear Algebra 2

Linear Algebra 2

Students are familiarised with and master the basic concepts and propositions of linear algebra and complex numbers. They can formulate simple concrete questions in the mathematical language and are able to solve these independently. // Teaching language: DE

Analysis 2

Analysis 2

Basic concepts and methods of differential and integral calculus of one real variable, as well as their application. // Teaching language: DE

Semester 3 , Semester ECTS : 22 , Semester week lessons : 22

Communication Competence 3

Communication Competence 3

The Communication Competence 3 module focuses on the following aspects of communication training: Communication in international and interdisciplinary settings // Mediation and transfer between English and German // Negotiation and discussion // Teaching language: DE/EN

Software Projects

Software Projects

In this first project module, the knowledge from other modules is applied and enriched with initial experiences of project work in a team. This includes requirements specification, software design, technical writing, teamwork, project planning and code management. // Teaching language: DE

Data Products and Services

Data Products and Services

The DPS module covers the basics of process management and business operations in relation to service management and the documentation and communication of services. // Teaching language: DE

Explorative Data Analysis

Explorative Data Analysis

The module Exploratory Data Analysis introduces the basics of descriptive statistics. In this module, the students learn to perform descriptive data analyses, which includes preparing, visualizing and describing the data with key figures using the statistical software R. // Teaching language: DE

Analysis 3

Analysis 3

In this module, students learn about linear ordinary differential equations and systems of first-order ODEs. In addition, the basic properties and calculus of functions of several variables are discussed. // Teaching language: DE

Electronics and Digital Technology: Basic Principles

Electronics and Digital Technology: Basic Principles

The physical principles of sensors are discussed, taking examples. Based on the laws of physics, the processes of measurement, the processing of raw data and the relationship of this data to data-based models, are explored both theoretically and experimentally. // Teaching language: DE

Semester 4 , Semester ECTS : 22 , Semester week lessons : 22

Digitisation of Economic Systems

Digitisation of Economic Systems

Digitalisation is profoundly changing our entire economic system. This lecture examines the concrete effects of digital transformation on the business models of established and new companies. The opportunities arising from digitisation and the obstacles or risks that exist are examined. // Teaching language: DE

Data Processing with R

Data Processing with R

Preparing, cleansing and visualising data are central tasks of a data scientist. In this module students train and consolidate the necessary skills in project teams, which they have acquired in the modules Explorative Datenanalyse and Datenbanken. // Teaching language: DE

Data Engineering 2

Data Engineering 2

Data Engineering topics are essential components of successful data products and data projects. Students learn the requirements for running successful data engineering pipelines, the key methods, and both the theoretical foundations and practical implementation of different methods and applications. // Teaching language: DE

Digital Entrepreneurship

Digital Entrepreneurship

Students learn basic models and methods for achieving entrepreneurial success with Smart Connected Products and digital services. Alongside this, the acquired knowledge is implemented by way of example with start-up companies and also in innovative projects in established companies. // Teaching language: DE

Probability Calculations

Probability Calculations

The module Probability Theory introduces the basics of probability theory. In this module, students learn to describe random events and their properties with probability models and to quantify them with the statistical software R. // Teaching language: DE

Numerics

Numerics

This class gives an introduction to the theory and algorithms of numerical mathematics. // Teaching language: DE

Semester 5 , Semester ECTS : 22 , Semester week lessons : 22

Elective Module

Elective Module

Text Processing

Text Processing

In this module, three small projects are carried out in the field of automatic text processing. In the first two projects, students familiarise themselves with the topic and methodology, in the third they implement a project independently. // Teaching language: DE

Operating Systems and Infrastructure

Operating Systems and Infrastructure

Efficient use of data and computationally-intensive applications requires basic operating system concepts to be understood. Students use remote virtualised infrastructure and services for data processing, creating and linking cloud services to run data- or computationally-intensive applications. // Teaching language: DE

Algorithms and Data Structures

Algorithms and Data Structures

Students cover basic algorithms and data structures of computer science. They learn to solve algorithmically complex tasks, know the central data structures of computer science and how they are implemented in Python, and can estimate the computational effort of programs and apply basic design patterns. // Teaching language: DE

Basics of Statistics

Basics of Statistics

Basics of statistics introduces students to the fundamentals of statistical inference, i.e. techniques that allow to draw inferences about a population from a sample. Special emphasis is put on computational methods that allow for the theoretical concepts to be applied in practice. // Teaching language: DE

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Semester 6 , Semester ECTS : 22 , Semester week lessons : 22

Elective Module

Elective Module

Big Data Project

Big Data Project

Students gain practical experience of working with Big Data problems. Based on the theoretical foundations of “Data Engineering 1” and “Data Engineering 2”, students analyse selected topics from these foundation courses and implement scalable applications using the latest Big Data technologies. // Teaching language: DE/EN

Front End, Web and Software Engineering

Front End, Web and Software Engineering

Students learn different frontend development technologies, starting with an introduction to GUI development in Python with PyQt and Matplotlib. Afterwards, the focus is on the development of web pages for showing charts, employing technologies like HTML, CSS and JavaScript. // Teaching language: DE

Machine Learning und Data Mining

Machine Learning und Data Mining

Machine learning and data mining are essential components of successful data products and projects. Students are familiarized with the prerequisites for their use and with various methods for different applications. They study the theoretical fundamentals and the implementation of the methods. // Teaching language: DE

Statistical Modelling

Statistical Modelling

The module introduces students to the basics of statistical modelling using linear regression analysis. Aspects of the model structure, inference, prediction, residuals analysis and model building, including variable selection, are examined in detail, both theoretically and in case studies. // Teaching language: DE

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Semester 7 , Semester ECTS : 24 , Semester week lessons : 18

Elective Module

Elective Module

Project Thesis: Data Science

Project Thesis: Data Science

Students work independently, typically in teams of two, on a concrete scientific/technical question under the guidance of a lecturer. The topic of the project work should come from the field of data science. The written final report includes the project implementation and the results obtained. // Teaching language: DE

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Optional Module

Optional Module

  • ECTS : 4

Module from group

You choose a elective module based on your interests.

Semester 8 , Semester ECTS : 24 , Semester week lessons : 12

Bachelor Thesis: Data Science

Bachelor Thesis: Data Science

Students work independently on a concrete scientific/technical question under the guidance of a lecturer. The topic of the project work should come from the field of data science. The Bachelor thesis is typically prepared by a team of two students, with a written final report. // Teaching language: DE

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

Elective Module

Elective Module

  • ECTS : 4

Module from group

  • Software engineering / IT
  • Information Engineering
  • Artificial intelligence
  • Multimedia
  • Robotics, industrial manufacturing, automation
  • Signal and information processing, digital signal processing
  • (Data-based) service engineering
  • Quantitative methods in marketing
  • Finance/Banking
  • Digital Health
  • Machine and system data
  • Mobility Data
  • Computational Life Sciences

The list of elective modules reflects the current state of the offer. It can change until the third year of study.

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The requirements for admission to the Bachelor programme at the ZHAW School of Engineering.

Click here for the admission requirements

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Learn how you can best prepare yourself for the Bachelor programme.

Preparing yourself for the programme

Application for the Bachelor programme

Admission requirements

How to prepare yourself for the Bachelor programme

Apply for the Bachelor programme today.

The requirements for admission to the Bachelor programme at the ZHAW School of Engineering.

Learn how you can best prepare yourself for the Bachelor programme.

Click here to apply

Click here for the admission requirements

Preparing yourself for the programme