Data-driven engineering

Key Facts:

Type: Lecture with exercise

Language: English

Cycle: Annually in the winter semester

Study program: Master

Target group: Master students of computer science, business informatics, computer engineering, mechanical engineering, electrical systems engineering, industrial engineering, and business administration

Aim of the course:

The goal of the lecture is to provide a comprehensive overview of the potentials and use cases in data-driven engineering. Important fundamentals and concepts from the fields of engineering and artificial intelligence are introduced and explained using meaningful practical examples. The acquired knowledge is deepened and implemented in exercises. As part of a group project, participants will develop their own functional engineering assistant.

Content:

Data is the oil of the 21st century. Data is also becoming increasingly important in product development. Both field data and development data can be processed using modern data analysis methods and AI processes to increase the efficiency and effectiveness of product development. The lecture provides an overview of the challenges and possible solutions of Data-driven Engineering. Theoretical principles and concepts are introduced and exemplary applications from practice are presented. The process is considered from data acquisition to possibilities for data evaluation and the development of innovative assistance systems. The acquired knowledge is deepened and implemented in the exercises. Contents of the course are:

  • Motivation and definition of terms
  • Potentials of data-driven engineering
  • Engineering IT and data management along the product life cycle
  • Fundamentals of data analytics and AI (in particular generative AI)
  • Data structures and formats in product development
  • Application examples and assistance systems (co-pilots) along the product life cycle (from requirements engineering to production planning)
  • Methods for planning and implementing Data-driven Engineering use cases
  • Technical development of assistance systems (co-pilots) in Data-driven Engineering

Qualification goals:

The participants will ...

  • recognize and evaluate the potential of Data-driven Engineering.
  • evaluate requirements for the application of Data-driven Engineering concepts.
  • analyze and design engineering IT infrastructures.
  • plan and implement use cases for Data-driven Engineering.
  • design assistance systems (co-pilots) for Data-driven use cases.
  • be able to present work results to other participants.
  • be able to work in an interdisciplinary team.

Written examination (100%).

Students can receive bonus points by participating in a testate as well as a practical group project.

Dates and registration deadlines:.

Please refer to the campus management system PAUL for dates and registration deadlines.

Con­tact per­son:

Benjamin Tiggemann

Advanced Systems Engineering / Heinz Nixdorf Institut

Systems Engineering

Write email +49 5251 60-6273