Data Engineering (B.Eng.)

Bachelor’s degree program | Department VI

Degree: Bachelor of Engineering (B.Eng.)
Duration: 6 semesters
Start: Winter semester
Admission: NC
Teaching language: English
Note: New study program starting in the winter semester of 2026/27
ECTS credits: 180

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Why study Data Engineering?

What is data engineering—explained simply? You’ll learn to build systems that process large amounts of data and turn it into something useful—for example, recommendations in online stores (these pants go well with these shoes) or automated analyses (we need to order more T-shirts because the weather is about to change).

Why is this relevant right now? More and more things are happening online: shopping, communication, mobility. This generates huge amounts of data. Companies need systems to use this data—and people to build those systems.

What exactly will I do during my studies? You’ll program, build systems, and work with data. Step by step, you’ll develop applications that function similarly to real-world systems in companies.

Here’s what you should bring

No prior knowledge of computer systems or machine learning is required.

You should be interested in understanding how systems work and how technology is built and operated. You need:

  • A desire to learn
  • Motivation to solve problems
  • A willingness to engage with technology

Requirements

  • English at B2 level
  • German at A2 level
  • Higher education entrance qualification

Study

The Bachelor Data Engineering is a 6-semester, interdisciplinary on-site program based on five pillars:

  • programming
  • distributed systems
  • databases
  • mathematics and statistics
  • and machine learning

The program follows a structured learning progression, with each semester building specific competencies.

After the first semester (“settle in”), you will have acquired the fundamental skills in programming and mathematics, for example through modules such as Introduction to Computer Science (based on Harvard CS50) and Math for Programmers. This semester aligns different prior knowledge levels and requires no previous experience.

After the second semester (“scale up”), you will understand parallel data processing within a single machine. You will learn how to use multiple CPU cores efficiently and strengthen your mathematical and statistical skills.

After the third semester (“scale out”), you will design and work with distributed systems that process large datasets. You will gain experience with distributed data pipelines and databases and further develop your foundation for machine learning.

After the fourth semester (“synthesize”), you will apply your knowledge in domain-specific contexts. Through electives, you will work in areas such as robotics, civil engineering, life sciences, building technology, or geoinformation with experts from other BHT departments.

After the fifth semester (“apply”), you will complete a full-time industry internship of approximately 23 weeks. You will work on real projects in a company of your choice and gain practical experience in professional environments.

After the sixth semester (“finish”), you will complete your Bachelor’s degree. You will deepen your knowledge in advanced topics such as Deep Learning or Large Language Models and prepare for further academic study, for example in a Master’s program in Data Science.

You will work on real data engineering problems: designing data pipelines, processing large datasets, and operating reliable machine learning systems. These are the same challenges faced by companies such as Zalando, SAP, or Amazon. Through projects, modern tools, and the industry internship, you gain experience with production systems rather than simplified examples.

Graduates are well prepared for roles such as Data Engineer, Machine Learning Engineer, or Backend Engineer. These roles are in high demand across industries, including e-commerce, healthcare, finance, and mobility. The program also provides a strong foundation for pursuing a Master’s degree.

What sets us apart:

You can start the program without prior experience in computer science. In the first semester, you will build the foundations in programming and mathematics and align your knowledge with that of your peers.

The program focuses on building and operating real-world data systems. You will learn how to design scalable data pipelines and distributed systems from early on. The structured progression - from foundations to parallel and distributed systems to applications - ensures continuous skill development.

You will apply your knowledge in interdisciplinary contexts and gain practical experience across domains. A key component is the extended industry internship, where you will work on real projects. Combined with teaching that integrates research and industry experience, this prepares you to contribute quickly in professional engineering environments and supports both career entry and further academic study.

Curriculum

Semester 1
Module Module Titel SU SWS Ü SWS LP P/WP FB
PR1 Programming 1 2 2 5 P VI
PR2 Programming 2 2 2 5 P VI
CT1 Computational Tools in Engineering 1 2 2 5 P II
MAP Math for Programmers 2 2 5 P II
CSI Computer Systems Introduction 2 2 5 P VI
SG1 General Studies 2   2,5 WP I
SG2 General Studies   2 2,5 WP I
Semester 2
Module Module Titel SU SWS Ü SWS LP P/WP FB
PR3 Programming 3 2 2 5 P VI
PR4 Programming 4 2 2 5 P VI
CT2 Computational Tools in Engineering 2 2 2 5 P II
ID1 Information from Data 1 2 2 5 P II
DS1 Distributed Systems 1 2 2 5 P VI
DB1 Relational Databases 2 2 5 P VI
Semester 3
Module Module Titel SU SWS Ü SWS LP P/WP FB
MLC Machine Learning Concepts 4   5 P VI
MLO Machine Learning Operations 2 2 5 P VI
DTP Data Protection and Bias 2   5 P VI
ID2 Information from Data 2 2 2 5 P II
DS2 Distributed Systems 2 4   5 P VI
DB2 Applied Databases 2 2 5 P VI
Semester 4
Module Module Titel SU SWS Ü SWS LP P/WP FB
MLA Machine Learning Applications 2 2 5 P VI
DMA Data Mining Applications 2 2 5 P VI
DB3 Cloud Databases 4   5 P VI
ES1 Elective Subject 1   4 5 WP VI
ES2 Elective Subject 2   4 5 WP VI
ES3 Elective Subject 3   4 5 WP VI
Semester 5
Module Module Titel SU SWS Ü SWS LP P/WP FB
INT Internship     30 P VI
Semester 6
Module Module Titel SU SWS Ü SWS LP P/WP FB
ES4 Elective Subject 4   4 5 WP VI
ES5 Elective Subject 5   4 5 WP VI
PRD Paper Reading Seminar 2   5 P VI
BTC Bachelor's Thesis Colloqium   1 3 P VI
BTX Bachelor's Thesis and Oral Final Exam     12 P VI

Note: Additional modules may be designated as elective subjects.

Wahlpflichtmodule (WP)
Modul Modultitel Semester SU SWS Ü SWS LP P/WP FB
DEP Dependable Systems 6   4 5 WP VI
LLM Large Language Models 6   4 5 WP VI
EMB Embedded Artifical Intelligence 4   4 5 WP VI
VIS Computer Vision 4   4 5 WP VI
DEL Deep Learning 6   4 5 WP VI
BEN Systems Benchmarking 6   4 5 WP II
GEO Geoinformatics 4   4 5 WP III
SBI Smart Building Infrastructure 4   4 5 WP IV
BIO Systems Biology 4   4 5 WP V
ROB Robotics 4   4 5 WP VII
ENE Ethics in Data Engineering 6   4 5 WP I
EX1 External Course 1 4 - - 5 WP Studiengangsleitung
EX2 External Course 2 6 - - 5 WP Studiengangsleitung

Source: Amtliche Mitteilung 14/2026, dated 01 July 2025

SWS: Hours per week (Semesterwochenstunden), SU: Seminar (Seminaristischer Unterricht), Ü: Practice (Übung), P: Required Module (Pflichtmodul), WP: Required-Elective Module (Wahlpflichtmodul), LP: Credits (Leistungspunkte), FB: Department (zuständiger Fachbereich)