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School of Engineering

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. September 2025

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Context Modules

Project Modules

Subject-Specific Modules

Mathematics and Nat. Science Modules

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

Digitisation of Economic Systems

Digitisation of Economic Systems

Digitalization is leading to a profound transformation of the entire economic system. This lecture examines the question of how companies should develop competitive digitization strategies in order to exploit the opportunities of digitization. It will shed light on how digitalization strategies affect business models. // Teaching language: DE

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 to visualize data and describe them with key figures. Students learn how to carry out descriptive data analyses using the statistical software R. This includes preparing, visualizing, and describing the data with key figures. // 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, 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

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, ECTS: 30, Semester week lessons: 30

Elective Module

Elective Module

  • ECTS: 2

You have the option to choose from a portfolio of various communication modules.

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

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

Data Products and Services

Data Products and Services

The DPS module covers analytical principles for the management of data products and services. Methods for the management of service potentials (e.g. implementation of price differentiation, queue management), service processes (e.g. business process modelling, derivation of business process models from data) and service performance (e.g. modelling of customer lifetime value) are discussed. In addition, students will be able to implement the presented methods and algorithms in Python using concrete application scenarios and sample data. // 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

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

Analysis 3

Analysis 3

In this module, students learn about linear ordinary differential equations and systems of firstorder ODEs. In addition, the basic properties and calculus of functions of several variables are discussed. Moreover, the basic concepts of Fourier analysis are explained and applied to examples. // Teaching language: DE

Physical Principles of Sensor Technology

Physical Principles of Sensor Technology

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, ECTS: 30, Semester week lessons: 30

Elective Module

Elective Module

  • ECTS: 2

You have the option to choose from a portfolio of various communication modules.

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

Introduction to Natural Language Processing

Introduction to Natural Language Processing

This module introduces the basic methods and technologies of Natural Language Processing (NLP). Typical tasks and solution approaches are presented and implemented based on practice-oriented projects. // 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 and Data Mining 2

Machine Learning and Data Mining 2

We focus on the theory and practical application of deep learning (DL). We start with the DL foundations and dive into the details of the most frequently used modern deep learning models, their capabilities and limitations and their application on real-world use cases from different domains. // 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, ECTS: 30, Semester week lessons: 24

Elective Module

Elective Module

  • ECTS: 2

The context modules offer you the opportunity to acquire supplementary skills outside of your technical major. They cover the following topics: Sustainability and Environment // Innovation and Technology Management // Data Science and AI // Business and Finance // Social Sciences and Intercultural Competence // Law and Governance // Project and Conflict Management // Resilience and Risk Management // Mobility and Transportation

Elective Module

Elective Module

  • ECTS: 2

The context modules offer you the opportunity to acquire supplementary skills outside of your technical major. They cover the following topics: Sustainability and Environment // Innovation and Technology Management // Data Science and AI // Business and Finance // Social Sciences and Intercultural Competence // Law and Governance // Project and Conflict Management // Resilience and Risk Management // Mobility and Transportation

Project Thesis: Data Science

Project Thesis: Data Science

The project thesis consists of working independently on a concrete scientific question or engineering problem from the field of data science. // Teaching language: DE

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Interdisciplinary elective module

Interdisciplinary elective module

  • ECTS: 4

The interdisciplinary elective modules cover a wide range of disciplines, including the following areas: Application of Advanced Mathematics in Engineering Practice // Introduction to Machine Learning // Modern Physics // Blockchain Technologies and Their Applications // Digital Assistance Technologies // Digital Health // Sensor Technology // Fundamentals of Digital Signal Processing of Sensors // Sustainability Management // Safety-Critical Systems // System Dynamics Modeling // Future Technologies of the Internet of Things // Safety and Systems Engineering

Semester 6, ECTS: 30, Semester week lessons: 18

Elective Module

Elective Module

  • ECTS: 2

The context modules offer you the opportunity to acquire supplementary skills outside of your technical major. They cover the following topics: Sustainability and Environment // Innovation and Technology Management // Data Science and AI // Business and Finance // Social Sciences and Intercultural Competence // Law and Governance // Project and Conflict Management // Resilience and Risk Management // Mobility and Transportation

Bachelor Thesis: Data Science

Bachelor Thesis: Data Science

The bachelor thesis consists of working independently on a concrete scientific question or engineering problem from the field of data science. // Teaching language: DE

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

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. September 2025

Legend

Context Modules

Subject-Specific Modules

Mathematics and Nat. Science Modules

Project Modules

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

Digitisation of Economic Systems

Digitisation of Economic Systems

Digitalization is leading to a profound transformation of the entire economic system. This lecture examines the question of how companies should develop competitive digitization strategies in order to exploit the opportunities of digitization. It will shed light on how digitalization strategies affect business models. // 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

Explorative Data Analysis

Explorative Data Analysis

The module Exploratory Data Analysis introduces the basics of descriptive statistics to visualize data and describe them with key figures. Students learn how to carry out descriptive data analyses using the statistical software R. This includes preparing, visualizing, and describing the data with key figures. // 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, ECTS: 22, Semester week lessons: 22

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 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

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, ECTS: 22, Semester week lessons: 22

Elective Module

Elective Module

  • ECTS: 2

You have the option to choose from a portfolio of various communication modules.

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 analytical principles for the management of data products and services. Methods for the management of service potentials (e.g. implementation of price differentiation, queue management), service processes (e.g. business process modelling, derivation of business process models from data) and service performance (e.g. modelling of customer lifetime value) are discussed. In addition, students will be able to implement the presented methods and algorithms in Python using concrete application scenarios and sample data. // 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

Analysis 3

Analysis 3

In this module, students learn about linear ordinary differential equations and systems of firstorder ODEs. In addition, the basic properties and calculus of functions of several variables are discussed. Moreover, the basic concepts of Fourier analysis are explained and applied to examples. // Teaching language: DE

Physical Principles of Sensor Technology

Physical Principles of Sensor Technology

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, ECTS: 22, Semester week lessons: 22

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

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

Numerics

Numerics

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

Elective Module

Elective Module

  • ECTS: 2

You have the option to choose from a portfolio of various communication modules.

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

Elective Module

Elective Module

  • ECTS: 2

The context modules offer you the opportunity to acquire supplementary skills outside of your technical major. They cover the following topics: Sustainability and Environment // Innovation and Technology Management // Data Science and AI // Business and Finance // Social Sciences and Intercultural Competence // Law and Governance // Project and Conflict Management // Resilience and Risk Management // Mobility and Transportation

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

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

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

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

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

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

Elective Module

Elective Module

  • ECTS: 2

The context modules offer you the opportunity to acquire supplementary skills outside of your technical major. They cover the following topics: Sustainability and Environment // Innovation and Technology Management // Data Science and AI // Business and Finance // Social Sciences and Intercultural Competence // Law and Governance // Project and Conflict Management // Resilience and Risk Management // Mobility and Transportation

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

Introduction to Natural Language Processing

Introduction to Natural Language Processing

This module introduces the basic methods and technologies of Natural Language Processing (NLP). Typical tasks and solution approaches are presented and implemented based on practice-oriented projects. // Teaching language: DE

Machine Learning and Data Mining 2

Machine Learning and Data Mining 2

We focus on the theory and practical application of deep learning (DL). We start with the DL foundations and dive into the details of the most frequently used modern deep learning models, their capabilities and limitations and their application on real-world use cases from different domains. // 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

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

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

Elective Module

Elective Module

  • ECTS: 2

The context modules offer you the opportunity to acquire supplementary skills outside of your technical major. They cover the following topics: Sustainability and Environment // Innovation and Technology Management // Data Science and AI // Business and Finance // Social Sciences and Intercultural Competence // Law and Governance // Project and Conflict Management // Resilience and Risk Management // Mobility and Transportation

Project Thesis: Data Science

Project Thesis: Data Science

The project thesis consists of working independently on a concrete scientific question or engineering problem from the field of data science. // Teaching language: DE

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Interdisciplinary elective module

Interdisciplinary elective module

  • ECTS: 4

The interdisciplinary elective modules cover a wide range of disciplines, including the following areas: Application of Advanced Mathematics in Engineering Practice // Introduction to Machine Learning // Modern Physics // Blockchain Technologies and Their Applications // Digital Assistance Technologies // Digital Health // Sensor Technology // Fundamentals of Digital Signal Processing of Sensors // Sustainability Management // Safety-Critical Systems // System Dynamics Modeling // Future Technologies of the Internet of Things // Safety and Systems Engineering

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

Bachelor Thesis: Data Science

Bachelor Thesis: Data Science

The bachelor thesis consists of working independently on a concrete scientific question or engineering problem from the field of data science. // Teaching language: DE

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

Elective Module

Elective Module

  • ECTS: 4

In the elective modules, you will acquire in-depth methodological skills, as well as application-specific knowledge for data science projects in the following domains: digital health // finance // Industrial IoT

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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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