Data Science & Management Bachelor

In your first module you’ll cover the basics of data science and business analytics — what they have in common, where they differ — and learn the foundations of academic work: sources, citations, structuring your thinking, and presenting research clearly.

Get to know how data science projects are structured and take your first steps in programming. You’ll learn the relevant tooling — Python, Pandas — and start applying it to real data.

Build the foundation for working with data: probability, linear algebra, calculus, descriptive statistics — the math you actually need to understand modern data science.

Communicate confidently in business — in English. Practise presentations, negotiations, written communication, and the vocabulary you’ll use in a digital workplace.

Understand the foundations of processing and handling data: how IT systems work, data structures, networks, and the technologies that underpin every digital business.

Inductive statistics: probability theory, hypothesis testing, and statistical inference. You’ll learn how to draw valid conclusions from data and avoid common pitfalls.

Build digital communication skills — how to present, visualize and tell stories with data so that stakeholders actually understand and act on it.

Work with structured and semi-structured data (SQL, JSON, XML), prepare datasets for analysis, and learn the data management practices used in production environments.

Data warehouses, ELT processes, OLAP techniques, dashboard design and reporting — the BI stack from data sources to decisions.

Strategies for self-management, resilience, time management and working effectively in a digital, fast-moving environment.

Explore the responsibilities and instruments of HR management — recruiting, development, leadership styles, organisational culture — and how they shape modern data-driven companies.

Fundamentals of digital marketing, marketing instruments, online channels, customer journeys, and the data analytics behind every marketing decision.

Learn the tools developers use day to day to deliver technical solutions: Git & GitHub, IDEs, testing, code review, and collaborative development workflows.

Cover the legal foundations of data work (data protection, IP, contracts) and the ethical considerations that come with building and deploying data-driven systems.

Data is the new gold — in this module you’ll learn how to extract value from it: exploratory analysis, deriving insights, and translating them into business recommendations.

Focus on organising and steering operational processes: process modelling, optimisation, automation, and the digital tools that drive transformation.

Learn the key Python packages used for machine learning — from scikit-learn to modern deep-learning frameworks — and apply them to real prediction and classification problems.

Dive into one of our specialisation tracks (e.g. Generative AI, Trustworthy AI, Data Engineering). You’ll choose your specialisation modules from our growing catalogue.

This module introduces qualitative and quantitative research methods. You’ll learn how to design studies, collect data, evaluate results, and present findings rigorously.

Continue with a second specialisation module — go deeper or broaden your profile in another area of applied data science.

Learn the theory and practice of project management — from classical to agile (Scrum, Kanban) — and how to make teams work well together on real projects.

Master the essential tools for communicating and visualising data — dashboard design, narrative reporting, modern visualisation libraries — to make insights land with decision-makers.

Understand the structure, core functions and tasks of financial accounting — including cost and performance accounting — to read company numbers fluently.

Round out your specialisation profile with a third module — combine technical depth with the focus area most relevant to your career path.

Each year we offer a rotating selection of electives — pick the topics that broaden your perspective and complement your specialisation.

Take your fourth specialisation module to round out your profile — go deep on the area that defines your career direction.

Choose another elective to broaden your perspective and complement the focus of your final semester.

This module supports you in writing your bachelor’s thesis — academic writing, structure, methodology, and a colloquium where you discuss your work with peers and advisors.

You’ve (almost) made it. Now it’s time to write a substantial piece of research or applied work that brings together everything you’ve learned — and earns you your degree.