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If you want to understand how data helps explain the world and how it can be used to solve real-world challenges, the Bachelor’s programme in Data Science in Engineering and Business is the ideal starting point. In the first semesters, you build a strong foundation in mathematics, statistics, and engineering-oriented computer science, complemented by technical and business subjects that help you interpret data in the right context and apply it to practical problems.
From the third semester onwards, you deepen your expertise in areas such as Applied Machine Learning, Data Engineering, and other application-driven disciplines. You will learn how to develop predictive models, design data pipelines, and integrate data science into real-world processes across fields ranging from product development and digital marketing to engineering applications. Elective modules such as Practical Deep Learning, Statistical Quality Assurance, Forecasting, and Ethics in Data Science allow you to tailor your studies to your interests. Hands-on projects and close collaboration with partners such as AImotion Bavaria ensure that you gain valuable experience applying data science under real-world conditions.
<p>This module provides core mathematical tools for data science, including calculus, functions and differential equations. Students learn formal reasoning and how to analyze and solve typical data-driven problems using mathematical methods.</p>
<p>Introduces statistical thinking and data analysis. Covers descriptive statistics, probability, distributions, estimation and hypothesis testing, enabling students to analyze data correctly and draw reliable conclusions.</p>
<p>Provides essential computer science skills for data science practice, including file systems, operating systems, command-line tools, IT security and cloud environments as a foundation for efficient data work.</p>
<p>Deepens statistical knowledge through hands-on application. Students analyze real datasets, create visualizations, perform simulations and interpret results within small project-based assignments.</p>
<p>Introduces applied programming with Python, Bash and SQL. Focuses on data analysis, relevant libraries, server-based work and practical handling of databases<span style="-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);color:rgb(0, 0, 0);display:inline !important;float:none;font-family:"Segoe UI";font-size:14px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:pre-wrap;widows:2;word-spacing:0px;">.</span></p>
<p>Conveys fundamental knowledge of business administration and economics and promotes holistic managerial thinking. Students learn to analyze entrepreneurial processes based on facts and to make goal-oriented decisions.</p>
<p>Building on Mathematics 1, this module covers linear algebra and multivariable calculus, providing essential foundations for data science methods such as optimization, machine learning and statistical modeling.</p>
<p>Introduces statistical modeling with a focus on linear regression. Students learn to assess model assumptions, evaluate model quality and select appropriate models for data-driven problems.</p>
<p>Deepens software development skills using Python, covering object-oriented programming, testing, debugging, basic algorithms and collaborative development of data-driven applications.</p>
<p>Practical implementation of statistical models using real-world data. Focuses on data preparation, modeling, diagnostics and interpretation of regression models, including teamwork and presentation.</p>
<p>Project-based module on professional software development. Students work with version control, testing, documentation and containerization to collaboratively build deployable data-driven applications.</p>
<p>An introduction to the development of technical products, as well as the design and standard-compliant representation of components and assemblies. In addition to a systematic approach to the product development process—from concept to production—students learn how to select tolerances, fits, and surface characteristics appropriate for the intended function, how to perform tolerance analyses, and how to design for manufacturability. </p>
<p>Provides theoretical foundations of probability, including random variables, distributions and limit theorems, enabling probabilistic modeling of data-driven processes.</p>
<p>Introduces applied machine learning, covering classification methods, model evaluation, validation, interpretability and responsible use of ML models.</p>
<p>Introduces data engineering fundamentals, focusing on relational databases, data modeling and SQL, and integrating structured data into software projects.</p>
<p>Practice-oriented ML project module where students implement complete ML workflows from data preparation and model training to evaluation and team-based presentation.</p>
<p>Introduction to industrial automation systems, covering control engineering, sensors, PLC programming and industrial communication in modern production environments.</p>
<p>The Production Engineering module provides fundamental knowledge about machines, production and assembly systems, and their use in industry. It also covers digital transformation, sustainability, and the economic as well as organizational aspects of modern production processes.</p>
<p>Applies statistical methods to quality assurance in production and services, covering process control, control charts, process capability and design of experiments with strong practical relevance.</p>
<p>This module teaches practical deep learning methods. Students build neural networks for image, text and sequence data using modern frameworks such as PyTorch or TensorFlow in realistic use cases.</p>
<p>Deepens data engineering concepts with a focus on big data and NoSQL. Covers data types, scalable data models, data preparation and cloud-based distributed systems in practical projects.</p>
<p>Project-based lab for applying deep learning. Students design, train and evaluate complex models and document their results scientifically in a team setting.</p>
<p>Module in English. Introduction to business information systems and IT infrastructures. Covers ERP, CRM and SCM systems and their role in digital, data-driven business processes.</p>
<p>Teaches fundamentals and tools of marketing. Students learn to support market analysis, research and marketing decisions using data-driven approaches.</p>
<p>The internship semester deepens the skills acquired during theoretical coursework. It allows students to apply what they have learned and to gain professional experience. The 20-week internship must be performed during the second phase of the course after meeting the prerequisites for advancement. </p>
<p>A three-day intensive course focused on professional skills, including field trips, workshops, and seminars on topics such as facilitation, presentation, conflict management, public speaking, academic writing, and ethics.</p>
<p>Module in English. Introduction to mathematical optimization. Covers modeling, solution methods and decision problems and shows how optimization is applied in data science and machine learning.</p>
<p>Module in English. This module covers forecasting and time series analysis. Students model trends, seasonality and uncertainty to generate predictions for technical and economic applications.</p>
<p>Team-based project with strong practical orientation. Students work on real-world problems at the intersection of data science, engineering and business.</p>
<p>Covers fundamentals of investment appraisal and corporate finance. Students evaluate investments, financing options and economic risks using data-based methods.</p>
<p>Covers ethical, legal and societal aspects of data science and AI, including data protection, fairness, transparency and regulation.</p>
<p>Independent work on a data-driven problem in engineering or business, applying learned methods and documenting results in a scientific manner.</p>
<p>Prepares students for the bachelor’s thesis by developing research questions, methodology and structure and practicing academic presentation and discussion.</p>
<p>Introduction to the Industrial Internet of Things. Covers connected systems, industrial communication, data acquisition and data-driven industrial applications.</p>
| 1. Semester | |
|---|---|
| 2. Semester | |
| 3. Semester | |
| 4. Semester | |
| 5. Semester | |
| 6. Semester | |
| 7. Semester |
In the first semesters, you will lay the foundations for your future career as a data scientist with courses such as maths, statistics and engineering informatics. These subjects form the pillars of data science and help you to understand and analyse complex data. The understanding of technical and economic processes that you gain through courses such as Engineering and Management enables you to master both technical challenges and recognise economic opportunities.
From the third semester onwards, you will deepen your knowledge through specialised courses such as Applied Machine Learning and Data Engineering. These courses are designed to teach you practical skills that will enable you to use modern technologies in real-life applications. Topics such as Digital Marketing and Product Development show you how you can use data science to make strategic decisions in various industries and develop innovative products.
In the higher semesters, you will have the opportunity to further develop your skills through elective modules and specialisation courses such as Forecasting in Engineering and Management, Practical Deep Learning, Statistical Quality Assurance and Ethics in Data Science. These courses prepare you for the latest challenges in technology and business and allow you to dive deep into complex topics that will shape the future of industry.
Application period for the winter semester is May 2nd to July 15th.
This course is held in German, so you'll need to prove a high level on German language (B2) for your application from abroad. We do not offer in-advance language courses for prospective applicants..
Please consider that the online application is only possible in this space of time. Applications that reach the university later than that cannot be taken into consideration.
Please find further information on application for bachelor study courses.
Nowadays, there are large amounts of data for every question, but only a few are able to recognise structures in the data and generate useful information. Even fewer people have additionally mastered the basic knowledge of technology and business so that they know what information is important in a company. This is exactly where the Data Scientist in technology and business comes in. Not only do they have the technical expertise, but they are also able to communicate the relevant facts clearly and understandably.
At our university, all subjects are taught in an applied and less theoretical way. As a prerequisite for the degree programme, you should have an interest in working with data and be interested in the application fields of Data Science.
Openness to new technologies - inclination to think analytically - ability to think your way into new problems - flexibility in thinking - curiosity - interest in real-world applications - motivation and initiative, if these are your strengths, you've come to the right place!
The consultancy McKinsey recently estimated the global demand for new data scientists at 1.5 million. There are probably few fields of study that are currently and will be in such demand for the foreseeable future as data science. Similar to a classic industrial engineer, you will not only bring expertise in the field of data science with you after this degree, but you will also understand the basics of technology and economics.
Pure data science degree programmes are usually located in the computer science faculties of universities. There, development skills in the field of data analysis are taught first and foremost. Students thus learn to develop algorithms and to programme in specific programming languages. In our Data Science in Engineering and Management degree programme, the focus is not on development skills, but on application skills.
Of course, you will also learn the basics of programming, but the focus is on the application and use of data-driven systems. In addition, your knowledge of technology and business means you know what Data Science is needed for in companies!
Do you have any questions about applications, admission, enrolment, student finance, accommodation or other general topics? If so, please use our contact form and select the relevant topic. This will ensure your enquiry is sent directly to the relevant contact person(s) and can be answered quickly and effectively.
Vice Dean of the Faculty of Engineering and Management
Student Ambassador Data Science
Student Ambassador Data Science
Bachelor of Science (B. Sc.)
The Bachelor’s degree programme in Business Information Systems combines business processes with modern information technology. The focus is on the digitalisation of businesses, data-driven business models, and the development and management of application systems at the interface between IT and business.
About the degree program
Bachelor of Engineering (B. Eng.)
International Industrial Engineering is designed for students who aspire to build a career in globally operating companies and international markets. Taught entirely in English, this Bachelor’s programme combines engineering expertise with management and business knowledge, providing a broad and future-oriented education. You will develop the technical, analytical, and leadership skills needed to address complex challenges across industries, preparing you for roles at the intersection of technology, innovation, and global business. With its international focus and interdisciplinary curriculum, the programme equips graduates to succeed in an increasingly connected world.
About the degree program
Bachelor of Engineering (B. Eng.)
At the core of Engineering and Business are engineering, business administration, production and project management. The curriculum connects product development, process optimisation and industrial value chains. Its specialist profile is shaped by technical and commercial perspectives combined in one interdisciplinary profile.
About the degree program
Bachelor of Science (B. Sc.)
The Artificial Intelligence Bachelor's programme combines the fields of computer science, data science and modern AI methods to provide a comprehensive education in intelligent systems. Key areas of focus include machine learning, computer vision, natural language processing and practical AI applications.
About the degree programIf you want to understand how data helps explain the world and how it can be used to solve real-world challenges, the Bachelor’s programme in Data Science in Engineering and Business is the ideal starting point. In the first semesters, you build a strong foundation in mathematics, statistics, and engineering-oriented computer science, complemented by technical and business subjects that help you interpret data in the right context and apply it to practical problems.
From the third semester onwards, you deepen your expertise in areas such as Applied Machine Learning, Data Engineering, and other application-driven disciplines. You will learn how to develop predictive models, design data pipelines, and integrate data science into real-world processes across fields ranging from product development and digital marketing to engineering applications. Elective modules such as Practical Deep Learning, Statistical Quality Assurance, Forecasting, and Ethics in Data Science allow you to tailor your studies to your interests. Hands-on projects and close collaboration with partners such as AImotion Bavaria ensure that you gain valuable experience applying data science under real-world conditions.
<p>This module provides core mathematical tools for data science, including calculus, functions and differential equations. Students learn formal reasoning and how to analyze and solve typical data-driven problems using mathematical methods.</p>
<p>Introduces statistical thinking and data analysis. Covers descriptive statistics, probability, distributions, estimation and hypothesis testing, enabling students to analyze data correctly and draw reliable conclusions.</p>
<p>Provides essential computer science skills for data science practice, including file systems, operating systems, command-line tools, IT security and cloud environments as a foundation for efficient data work.</p>
<p>Deepens statistical knowledge through hands-on application. Students analyze real datasets, create visualizations, perform simulations and interpret results within small project-based assignments.</p>
<p>Introduces applied programming with Python, Bash and SQL. Focuses on data analysis, relevant libraries, server-based work and practical handling of databases<span style="-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);color:rgb(0, 0, 0);display:inline !important;float:none;font-family:"Segoe UI";font-size:14px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:pre-wrap;widows:2;word-spacing:0px;">.</span></p>
<p>Conveys fundamental knowledge of business administration and economics and promotes holistic managerial thinking. Students learn to analyze entrepreneurial processes based on facts and to make goal-oriented decisions.</p>
<p>Building on Mathematics 1, this module covers linear algebra and multivariable calculus, providing essential foundations for data science methods such as optimization, machine learning and statistical modeling.</p>
<p>Introduces statistical modeling with a focus on linear regression. Students learn to assess model assumptions, evaluate model quality and select appropriate models for data-driven problems.</p>
<p>Deepens software development skills using Python, covering object-oriented programming, testing, debugging, basic algorithms and collaborative development of data-driven applications.</p>
<p>Practical implementation of statistical models using real-world data. Focuses on data preparation, modeling, diagnostics and interpretation of regression models, including teamwork and presentation.</p>
<p>Project-based module on professional software development. Students work with version control, testing, documentation and containerization to collaboratively build deployable data-driven applications.</p>
<p>An introduction to the development of technical products, as well as the design and standard-compliant representation of components and assemblies. In addition to a systematic approach to the product development process—from concept to production—students learn how to select tolerances, fits, and surface characteristics appropriate for the intended function, how to perform tolerance analyses, and how to design for manufacturability. </p>
<p>Provides theoretical foundations of probability, including random variables, distributions and limit theorems, enabling probabilistic modeling of data-driven processes.</p>
<p>Introduces applied machine learning, covering classification methods, model evaluation, validation, interpretability and responsible use of ML models.</p>
<p>Introduces data engineering fundamentals, focusing on relational databases, data modeling and SQL, and integrating structured data into software projects.</p>
<p>Practice-oriented ML project module where students implement complete ML workflows from data preparation and model training to evaluation and team-based presentation.</p>
<p>Introduction to industrial automation systems, covering control engineering, sensors, PLC programming and industrial communication in modern production environments.</p>
<p>The Production Engineering module provides fundamental knowledge about machines, production and assembly systems, and their use in industry. It also covers digital transformation, sustainability, and the economic as well as organizational aspects of modern production processes.</p>
<p>Applies statistical methods to quality assurance in production and services, covering process control, control charts, process capability and design of experiments with strong practical relevance.</p>
<p>This module teaches practical deep learning methods. Students build neural networks for image, text and sequence data using modern frameworks such as PyTorch or TensorFlow in realistic use cases.</p>
<p>Deepens data engineering concepts with a focus on big data and NoSQL. Covers data types, scalable data models, data preparation and cloud-based distributed systems in practical projects.</p>
<p>Project-based lab for applying deep learning. Students design, train and evaluate complex models and document their results scientifically in a team setting.</p>
<p>Module in English. Introduction to business information systems and IT infrastructures. Covers ERP, CRM and SCM systems and their role in digital, data-driven business processes.</p>
<p>Teaches fundamentals and tools of marketing. Students learn to support market analysis, research and marketing decisions using data-driven approaches.</p>
<p>The internship semester deepens the skills acquired during theoretical coursework. It allows students to apply what they have learned and to gain professional experience. The 20-week internship must be performed during the second phase of the course after meeting the prerequisites for advancement. </p>
<p>A three-day intensive course focused on professional skills, including field trips, workshops, and seminars on topics such as facilitation, presentation, conflict management, public speaking, academic writing, and ethics.</p>
<p>Module in English. Introduction to mathematical optimization. Covers modeling, solution methods and decision problems and shows how optimization is applied in data science and machine learning.</p>
<p>Module in English. This module covers forecasting and time series analysis. Students model trends, seasonality and uncertainty to generate predictions for technical and economic applications.</p>
<p>Team-based project with strong practical orientation. Students work on real-world problems at the intersection of data science, engineering and business.</p>
<p>Covers fundamentals of investment appraisal and corporate finance. Students evaluate investments, financing options and economic risks using data-based methods.</p>
<p>Covers ethical, legal and societal aspects of data science and AI, including data protection, fairness, transparency and regulation.</p>
<p>Independent work on a data-driven problem in engineering or business, applying learned methods and documenting results in a scientific manner.</p>
<p>Prepares students for the bachelor’s thesis by developing research questions, methodology and structure and practicing academic presentation and discussion.</p>
<p>Introduction to the Industrial Internet of Things. Covers connected systems, industrial communication, data acquisition and data-driven industrial applications.</p>
| 1. Semester | |
|---|---|
| 2. Semester | |
| 3. Semester | |
| 4. Semester | |
| 5. Semester | |
| 6. Semester | |
| 7. Semester |
In the first semesters, you will lay the foundations for your future career as a data scientist with courses such as maths, statistics and engineering informatics. These subjects form the pillars of data science and help you to understand and analyse complex data. The understanding of technical and economic processes that you gain through courses such as Engineering and Management enables you to master both technical challenges and recognise economic opportunities.
From the third semester onwards, you will deepen your knowledge through specialised courses such as Applied Machine Learning and Data Engineering. These courses are designed to teach you practical skills that will enable you to use modern technologies in real-life applications. Topics such as Digital Marketing and Product Development show you how you can use data science to make strategic decisions in various industries and develop innovative products.
In the higher semesters, you will have the opportunity to further develop your skills through elective modules and specialisation courses such as Forecasting in Engineering and Management, Practical Deep Learning, Statistical Quality Assurance and Ethics in Data Science. These courses prepare you for the latest challenges in technology and business and allow you to dive deep into complex topics that will shape the future of industry.
Application period for the winter semester is May 2nd to July 15th.
This course is held in German, so you'll need to prove a high level on German language (B2) for your application from abroad. We do not offer in-advance language courses for prospective applicants..
Please consider that the online application is only possible in this space of time. Applications that reach the university later than that cannot be taken into consideration.
Please find further information on application for bachelor study courses.
Nowadays, there are large amounts of data for every question, but only a few are able to recognise structures in the data and generate useful information. Even fewer people have additionally mastered the basic knowledge of technology and business so that they know what information is important in a company. This is exactly where the Data Scientist in technology and business comes in. Not only do they have the technical expertise, but they are also able to communicate the relevant facts clearly and understandably.
At our university, all subjects are taught in an applied and less theoretical way. As a prerequisite for the degree programme, you should have an interest in working with data and be interested in the application fields of Data Science.
Openness to new technologies - inclination to think analytically - ability to think your way into new problems - flexibility in thinking - curiosity - interest in real-world applications - motivation and initiative, if these are your strengths, you've come to the right place!
The consultancy McKinsey recently estimated the global demand for new data scientists at 1.5 million. There are probably few fields of study that are currently and will be in such demand for the foreseeable future as data science. Similar to a classic industrial engineer, you will not only bring expertise in the field of data science with you after this degree, but you will also understand the basics of technology and economics.
Pure data science degree programmes are usually located in the computer science faculties of universities. There, development skills in the field of data analysis are taught first and foremost. Students thus learn to develop algorithms and to programme in specific programming languages. In our Data Science in Engineering and Management degree programme, the focus is not on development skills, but on application skills.
Of course, you will also learn the basics of programming, but the focus is on the application and use of data-driven systems. In addition, your knowledge of technology and business means you know what Data Science is needed for in companies!
Do you have any questions about applications, admission, enrolment, student finance, accommodation or other general topics? If so, please use our contact form and select the relevant topic. This will ensure your enquiry is sent directly to the relevant contact person(s) and can be answered quickly and effectively.
Vice Dean of the Faculty of Engineering and Management
Student Ambassador Data Science
Student Ambassador Data Science
Bachelor of Science (B. Sc.)
The Bachelor’s degree programme in Business Information Systems combines business processes with modern information technology. The focus is on the digitalisation of businesses, data-driven business models, and the development and management of application systems at the interface between IT and business.
About the degree program
Bachelor of Engineering (B. Eng.)
International Industrial Engineering is designed for students who aspire to build a career in globally operating companies and international markets. Taught entirely in English, this Bachelor’s programme combines engineering expertise with management and business knowledge, providing a broad and future-oriented education. You will develop the technical, analytical, and leadership skills needed to address complex challenges across industries, preparing you for roles at the intersection of technology, innovation, and global business. With its international focus and interdisciplinary curriculum, the programme equips graduates to succeed in an increasingly connected world.
About the degree program
Bachelor of Engineering (B. Eng.)
At the core of Engineering and Business are engineering, business administration, production and project management. The curriculum connects product development, process optimisation and industrial value chains. Its specialist profile is shaped by technical and commercial perspectives combined in one interdisciplinary profile.
About the degree program
Bachelor of Science (B. Sc.)
The Artificial Intelligence Bachelor's programme combines the fields of computer science, data science and modern AI methods to provide a comprehensive education in intelligent systems. Key areas of focus include machine learning, computer vision, natural language processing and practical AI applications.
About the degree program