BSc Data Science

Overview

Statistics, data modelling and decision support in data-intensive organisations. The programme combines scientific foundations with project-based engineering practice, delivered in structured semesters with supervised teaching.

Curriculum

Illustrative curriculum. The officially approved module structure will be published once programme documentation is available.

1. Semester 1

   • Calculus I
   • Programming I (Python)
   • Linear Algebra
   • Probability and Statistics
   • Academic Skills and Scientific Writing

2. Semester 2

   • Calculus II
   • Algorithms and Data Structures
   • Database Systems
   • Data Mining
   • Technology Ethics and Responsibility

3. Semester 3

   • Optimisation
   • Machine Learning
   • Data Engineering and Pipelines
   • Data Visualisation
   • Foundation Laboratory

4. Semester 4

   • Deep Learning
   • Big Data Architectures
   • Time Series Analysis
   • Bayesian Statistics
   • Interdisciplinary Team Project

5. Semester 5

   • Natural Language Processing
   • Image Processing and Computer Vision
   • MLOps and Model Operations
   • Cloud Computing
   • Capstone Project

6. Semester 6

   • Industrial Placement Semester
   • Elective Modules
   • Bachelor's Thesis and Colloquium

Learning outcomes

• Apply scientific method to technical problems
• Design, model and evaluate complex systems
• Communicate results to specialist and international audiences
• Assess technological developments in ethical and legal terms

Admission requirements

• Recognised secondary qualification or equivalent — higher education entrance qualification including mathematics and one natural science (overall grade 2.5 or better)
• Evidence of proficiency in the language of instruction — English C1 (IELTS 7.0 / TOEFL iBT 95) or German C1 (TestDaF 4x4 / DSH-2), depending on the language of instruction
• Complete application documentation — transcripts, language certificate, CV and identity document

Career opportunities

• Development and design roles in technology-driven organisations
• Research and development in institutes and industry
• Project and systems responsibility in interdisciplinary teams
• Progression to master's or doctoral study