Data Science in Engineering and Management (B. Eng.)

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.

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.

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.

Deepens statistical knowledge through hands-on application. Students analyze real datasets, create visualizations, perform simulations and interpret results within small project-based assignments.

Introduces applied programming with Python, Bash and SQL. Focuses on data analysis, relevant libraries, server-based work and practical handling of databases.

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.

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.

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.

Deepens software development skills using Python, covering object-oriented programming, testing, debugging, basic algorithms and collaborative development of data-driven applications.

Practical implementation of statistical models using real-world data. Focuses on data preparation, modeling, diagnostics and interpretation of regression models, including teamwork and presentation.

Project-based module on professional software development. Students work with version control, testing, documentation and containerization to collaboratively build deployable data-driven applications.

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. 

Provides theoretical foundations of probability, including random variables, distributions and limit theorems, enabling probabilistic modeling of data-driven processes.

Introduces applied machine learning, covering classification methods, model evaluation, validation, interpretability and responsible use of ML models.

Introduces data engineering fundamentals, focusing on relational databases, data modeling and SQL, and integrating structured data into software projects.

Practice-oriented ML project module where students implement complete ML workflows from data preparation and model training to evaluation and team-based presentation.

Introduction to industrial automation systems, covering control engineering, sensors, PLC programming and industrial communication in modern production environments.

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.

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.

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.

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.

Project-based lab for applying deep learning. Students design, train and evaluate complex models and document their results scientifically in a team setting.

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.

Teaches fundamentals and tools of marketing. Students learn to support market analysis, research and marketing decisions using data-driven approaches.

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. 

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.

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.

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.

Team-based project with strong practical orientation. Students work on real-world problems at the intersection of data science, engineering and business.

Covers fundamentals of investment appraisal and corporate finance. Students evaluate investments, financing options and economic risks using data-based methods.

Covers ethical, legal and societal aspects of data science and AI, including data protection, fairness, transparency and regulation.

Independent work on a data-driven problem in engineering or business, applying learned methods and documenting results in a scientific manner.

Prepares students for the bachelor’s thesis by developing research questions, methodology and structure and practicing academic presentation and discussion.

Introduction to the Industrial Internet of Things. Covers connected systems, industrial communication, data acquisition and data-driven industrial applications.