The graduates of the course

  • are characterized by a fundamental and comprehensive specialist knowledge of modern information and communication technologies (especially with regard to the digital transformation of industry and society),
  • can design and implement complex workflows to merge and analyze large amounts of data from heterogeneous sources,
  • master the essential processes of data mining and machine learning in theory and practice,
  • have basic knowledge of data protection, IT security, the establishment of secure networks and quality assurance in the field of machine learning processes and
  • work with their own area of ??responsibility within larger teams made up of interdisciplinary computer scientists, engineers, business economists and other professional groups.



In the practice-integrated Bachelor's degree in Digital Technologies, practical phases in the company (eleven weeks) alternate with theoretical phases at the university (twelve weeks).

The students prepare for the theoretical phases during the practical phases using didactically prepared self-study materials. The compulsory modules offer a broad education in computer science, mathematics, business administration and interdisciplinary qualifications. In three practical modules and as part of the bachelor thesis, the students carry out practical projects. In this context, company-specific topics and contents can be deepened in order to prepare the students for the special tasks in operational practice. In the fifth and sixth semester, students have the opportunity to use an elective catalog to individualize their competence profile on topics from the fields of technical applications and business administration.

1st semester

  • Basics of computer science
  • Basics of
  • Data science and data protection
  • Mathematics i
  • Basics of Business Administration
  • Technical English

2 semesters

  • Algorithms and data structures
  • object oriented programing
  • Databases
  • Mathematics II
  • Operations Research

3rd semester

  • Big data
  • Cluster computing
  • HMI and user interfaces
  • statistics
  • Business process modeling and IT systems
  • Practice module I

4th semester

  • Machine learning
  • Data mining
  • Speech and image recognition
  • Web technologies
  • Basics of physics / electrical engineering

5th semester

  • Networking and IoT solutions
  • Business intelligence
  • Elective module *
  • Practice module II

6th semester

  • Assistance systems
  • Safety and Security
  • Elective module *
  • Practical module III

7th semester

  • Quality assurance for AI systems
  • Innovation and project management
  • bachelor thesis
  • colloquium

Elective modules

  • Control engineering
  • Industrial control technology
  • Sensors and actuators
  • Diagnosis and Predictive
  • Maintenance
  • Marketing and technical sales
  • Digital business models and value chains
  • Change management
  • Social Media and Natural Language Processing


Abitur or advanced technical college entrance qualification or previous education recognized as equivalent. For the practice-integrated course, evidence of an apprenticeship or internship or an employment relationship with a cooperating company is required.


Data scientists have in-depth knowledge of analyzing large amounts of data and, thanks to these cross-sectional qualifications, are just as much in demand in many areas of the economy as they are in research and administration. Occupational fields are, for example, online trading, search engines, manufacturing, the automotive or pharmaceutical industry, the financial sector or meteorology and climate research. In addition to analyzing the data, data scientists also drive the further development of the subject itself. You research, design new algorithms and create software that third parties use to implement applications.

Data engineering essentially comprises holding, managing and merging data. Data engineers penetrate the technical requirements of a project and are responsible for planning and developing a robust and flexible big data infrastructure, connecting internal and external data sources via batch, real-time and streaming interfaces and ensuring that
the Data.

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