PROGRAM:

B. Eng. Data Science and Artificial Intelligence

MODE:
Full-time, On-site
LEVEL:
Bachelor of Engineering
LANGUAGE :
English
DURATION :
7 semesters
LOCATION:
Munich, Siemens Neuperlach Campus
CREDITS :
210 ECTS
PRICE:
4.800 € / Semester
START DATE:
March 2026
October 2026
SCHOLARSHIPS:
Up to 80% for high-performing students.

The Bachelor of Engineering (B.Eng.) in Data Science and AI at MUDT is designed to equip students with the skills and knowledge to harness the power of data and artificial intelligence in the digital age.

This comprehensive program covers essential concepts, tools, and techniques, enabling students to analyze complex data, develop machine learning models, and create AI-driven solutions.

The curriculum is tailored to meet the growing demand for data professionals in various industries, preparing graduates for a successful career in a dynamic and evolving field.

Note: This program is being prepared for launch in October 2026

Specialisations

Specialisations

IoT, Edge AI & Industrial AI

AI integrated with connected devices, sensors, and industrial systems.

Recommended electives:

  • Cloud & Edge Computing
  • Industrial IoT
  • Machine-to-Machine Systems
  • Digital Image Capture
  • AI & Machine Learning

Career outcomes:
IoT Solutions Engineer, Smart Manufacturing Engineer, Edge AI Developer.

Cybersecurity & AI Safety (Cross-Program)

Use AI for secure systems and understand risks such as adversarial attacks.

Recommended electives:

  • CS-based elective modules (Cyber Security program)
  • Cloud & Edge Computing
  • Programming with AI
  • Ethics in AI

Career outcomes:
AI Security Specialist, Cyber-AI Analyst, AI Governance Consultant.

Machine Learning & Generative AI

Focus on advanced ML, deep learning, and modern AI systems.

Recommended electives:

  • Generative AI & LLMs
  • Agentic AI Systems
  • Programming with AI
  • Data Mining & Data Warehouse

Career outcomes:
Machine Learning Engineer, LLM Engineer, Generative AI Specialist.

Cloud, Big Data & MLOps

Build expertise in scalable AI deployments and distributed systems.

Recommended electives:

  • Big Data Analytics
  • Cloud & Edge Computing
  • Data Mining & Data Warehouse
  • Cross-program: Cloud/Security electives from CS or SE programs

Career outcomes:
Cloud & MLOps Engineer, Big Data Engineer, AI Infrastructure Engineer.

Business Intelligence & Data Analytics

Focus on visualization, dashboards, and applied business analytics.

Recommended electives:

  • Data Visualization
  • Data Visualization for BI
  • Data Mining & Data Warehouse
  • Cross-program: Business or industrial modules from DIE

Career outcomes:
BI Analyst, Data Analyst, Reporting Specialist.

Why Data Science and AI at MUDT?

Engineering-Based AI Education

You learn AI as an engineering discipline with strong foundations in math, algorithms, cloud systems, and software engineering — preparing you to build real, scalable AI solutions.

Modern Curriculum

The program includes Big Data, Cloud & Edge Computing, LLMs, Generative AI, Agentic AI, and Robotics, ensuring you graduate with skills aligned with today’s most advanced AI technologies.

Highly Flexible Specialisation

With eight electives and the option to choose courses from Cyber Security, Software Engineering, and Digital Industrial Engineering, you can tailor your study path to your career goals.

Practice-Focused Learning

Hands-on labs, agile EPIC projects, real datasets, and an industry internship ensure you gain practical experience and graduate with a strong portfolio of real-world AI work.

Responsible & Ethical AI Mindset

Integrated modules in AI Ethics, ESG, and Business Ethics prepare you to develop and deploy AI systems responsibly, meeting modern standards for fairness, transparency, and data protection.

Study Plan: B. Eng. Data Science and Artificial Intelligence

Below is a semester-by-semester breakdown of the study plan for the Data Science and AI program.

Semester 1 :
Building Core Competencies
30 ECTS
  1. Mathematics (Calculus & Linear Algebra)
  2. Scientific Working
  3. Network and Internet Technologies
  4. Software Applications and Technology
  5. Basics of Programming with Python
  6. Personal Development – Intercultural Communication
  7. Foreign Languages I
Semester 2 :
Expanding Knowledge Base
30 ECTS
  1. Basics of Database
  2. AI and Machine Learning
  3. Object-Oriented Programming
  4. Operating Systems
  5. Managing Projects and Tasks
  6. Career Planning
  7. Foreign Languages II
Semester 3 :
Specialized Foundations
30 ECTS
  1. Probability & Statistics
  2. AI & Machine Learning
  3. Software Engineering Techniques I
  4. Personal Development – Teaming Up
  5. Elective I (6 ECTS)
  6. Elective II (6 ECTS)
Semester 4 :
Advanced Theoretical and Applied Knowledge
30 ECTS
  1. Big Data Analytics
  2. Cloud & Edge Computing
  3. Ethics in AI & Data Science
  4. ESG & Sustainability
  5. Elective III
  6. Elective IV
Semester 5 :
Deepening Specialization
30 ECTS
  1. Data Visualization
  2. Generative AI & Large Language Models
  3. Basics of Information Security
  4. Career Planning II
  5. Elective V (6 ECTS)
  6. Elective VI (6 ECTS)
Semester 6 :
Practical Application
30 ECTS

Mandatory Internship: The internship integrates all program competencies in a real-world environment (AI engineering, data science, ML deployment, robotics, cloud/edge solutions, predictive systems, ethics and compliance).

Semester 7 :
Finalization and Career Readiness
30 ECTS
  1. Bachelor Thesis
  2. B.Sc. Seminar
  3. Elective VII
  4. Elective VIII
  5. Business Ethics and Entrepreneurship

Electives & Specialisation Paths

From semester 3 onwards you specialise through 8 electives (each 6 ECTS). Electives allow you to shape your profile toward specific AI domains and career targets, guided by Career Planning I/II.

1- Data Mining & Data Warehouse

Focus on large-scale data integration, ETL pipelines, database modelling, and mining techniques for predictive analytics.

2- Agentic AI Systems

Design of autonomous intelligent agents, multi-agent coordination, decision logic, and modern agent architectures used in LLM-based systems.

3- Programming with AI

AI-first programming skills, vibe-coding approaches, ML programming paradigms, NLP, computer vision, debugging, and MLOps fundamentals.

4- Humanoids (Robotics)

Human-inspired robot design, multi-DOF control, sensors & actuators, motion planning, adaptive behaviour, and human–robot interaction.

5- Data Visualization for Business Intelligence

Dashboarding, visual analytics, BI storytelling, advanced visualization tools, and communication of insights for decision-making.

Cross-Program Electives

In addition to program-specific electives, students also have the opportunity to choose from selected elective modules offered by other bachelor programs at MUDT, including:

  • B.Eng. Cyber Security

  • B.Eng. Software Engineering

  • B.Eng. Digital Industrial Engineering

This allows you to broaden your knowledge, gain interdisciplinary skills, and build a study profile that aligns even more closely with your professional ambitions.

Who Should Apply?

This program is ideal if you:

  • enjoy mathematics, data, technology, and solving complex problems,

  • are curious about artificial intelligence, machine learning, and intelligent systems,

  • want to work at the intersection of software + engineering + data,

  • like hands-on projects and building real AI-driven applications.

If you want to turn curiosity about data and technology into a future-ready engineering career, this program is an excellent match.

Qualification goals

1

Scientific & Analytical Thinking: Ability to apply scientific working methods, academic writing, and structured problem-solving techniques for technology-related questions.

2

Mathematical & Algorithmic Foundations: Understanding core mathematics, discrete structures, and algorithmic concepts essential for engineering and data-driven systems.

3

Digital Systems & Software Competence: Ability to work confidently with databases, networks, operating systems, and software tools as the technological backbone of modern digital systems.

4

Project Management & Teamwork: Application of agile methods (Scrum, Kanban), structured project execution, and effective collaboration in interdisciplinary teams.

5

Ethical, Sustainable & Responsible Use of Technology: Awareness of ethical frameworks, sustainability (ESG principles), and social responsibility when designing or deploying digital technologies.

6

Professional Communication in Global Environments: Capability to communicate technical ideas clearly to diverse audiences and collaborate effectively in multicultural, interdisciplinary settings.

1

Programming & Software for AI: Ability to develop AI applications using Python, R, and modern AI programming tools, including frameworks such as TensorFlow, PyTorch, and advanced agent-based libraries.

2

Mathematical & Statistical Foundations for ML: Mastery of probability, statistics, and linear algebra as the analytical base for machine learning and data modelling.

3

Machine Learning, Deep Learning & LLMs: Understanding and applying classical ML algorithms, neural networks, generative models, transformers, and large language models to real datasets.

4

Big Data, Cloud Computing & Edge AI: Skills in distributed data processing (e.g., Spark), cloud-native deployment (AWS, Azure, GCP), containerization (Docker, Kubernetes), and running AI on edge or IoT devices.

5

Engineering Problem-Solving with AI: Ability to integrate AI solutions into real engineering environments through structured experimentation, prototyping, and deployment.

6

Applied AI in Robotics, IoT, Cybersecurity & Predictive Systems: Applying AI to specialized domains such as robotics, autonomous systems, industrial engineering, predictive modelling, and secure AI architectures.

7

Ethical, Fair & Compliant AI Development: Ensuring transparency, fairness, data protection (GDPR), and responsible governance in all AI systems, including bias mitigation and explainability.

1

Communication, Collaboration & Intercultural Competence: Building strong interpersonal and cross-cultural communication skills, enabling effective teamwork in diverse global environments.

2

Creativity, Critical Thinking & Problem-Solving: Ability to think creatively, evaluate complex issues, and design thoughtful solutions for technical, social, or ethical challenges.

3

Ethical Reasoning & Responsible Decision-Making: Developing the capacity to evaluate the societal impact of technology and make responsible, ethical choices in professional contexts.

4

Leadership, Initiative & Project Ownership: Taking responsibility in group settings, showing initiative, and demonstrating leadership in project environments and long-term assignments.

5

Sustainability & Social Contribution: Understanding how digital technologies affect society and the environment, and acting in ways that support sustainable and socially conscious outcomes.

6

Professional Identity & Career Development: Gaining confidence in one’s career goals, leveraging career planning tools, and developing a long-term professional growth strategy.

7

Self-Management & Reflective Learning: Ability to manage time, reflect on learning experiences, and continuously improve personal and professional skills.

Career Paths & Prospects

Graduates of the B.Eng. Data Science & AI program are equipped for high-demand, well-paid roles across technology, engineering, and industry. With strong skills in machine learning, cloud systems, big data, robotics, IoT, and responsible AI, typical career paths include:

Data Scientist / Machine Learning Engineer
AI Developer / AI Systems Engineer
Cloud & Big Data Engineer
Robotics & Autonomous Systems Engineer
IoT & Edge AI Specialist
BI & Data Analyst
AI Ethics & Governance Consultant
Cybersecurity & AI Safety Engineer
Digital Twin & Industrial AI Engineer

Salaries & Market Outlook:

Germany offers one of Europe’s strongest job markets for AI talent:

Starting salaries: typically €50,000–€70,000 per year
Experienced roles: €70,000–€110,000+ depending on industry, location, and expertise
High demand: Germany faces a significant shortage of AI and data professionals
Fast growth: AI, cloud, robotics, IoT, and Industry 4.0 are expanding rapidly
Strong investment: Government and industry heavily support AI adoption, making this field one of the fastest-growing and best-paid technology sectors in the country
{ Take the next step toward becoming an AI engineer ready for real-world impact. }

Industry Cooperation

Faculty Expertise:

Learn from industry experts and researchers in machine learning, big data, cloud, robotics, cybersecurity, and ethical AI—bringing real-world experience directly into the classroom.
BIO

Ewa Currie, M.A.

BIO

Khaleeq Aziz

BIO

Dr. Arash Habibi Lashkari

BIO

Yılmaz Çankaya

BIO

Dr. Francisco Manuel Rangel Pardo

BIO

Dr. Jose Angel Gonzalez Barba

BIO

Dr. Marc Franco Salvador

BIO

Prof. dr. Jack Mochyla

BIO

Dr. Agnieszka Dziedzic

BIO

Prof. Dr. Pawel Gburzynski

How to apply

1

Submit Your Application:

Create an account, select your study program, and upload the required documents. We’ll confirm once everything is submitted.
2

Pass the Interview:

If your documents are approved, you'll be invited to an online interview. Succeed, and you'll receive a conditional acceptance.
3

Sign and Pay:

Return the signed study contract and complete the enrollment fee and deposit to secure your spot

Receive Your Admission Letter
Congratulations, you’re in!