Study data science alongside your job

Data science stands on three legs: statistics, programming and domain knowledge from the field the data comes from. Programmes cover probability, inferential statistics, machine learning, databases and visualisation. The goal is not to build software but to derive defensible statements from data and to say how uncertain they are.

This distinction decides your choice of subject:

• Computer science builds systems: architecture, algorithms, operating systems, software engineering. If you want to develop rather than analyse, that is the better fit, more on it under computer science by distance learning.
• Business informatics sits between the business side and IT: processes, system rollouts, requirements. Data analysis is a tool there, not the core.
• Data engineering builds the pipelines and the storage layer. In many companies it is the role that is actually vacant, because without clean data no model is worth anything.
• Business intelligence delivers figures and reports for decisions. The daily work sits closer to controlling than to research.

One point deserves to be said openly: certificates are a real competitor to a degree in this field. Courses from platform and cloud providers teach tools fast and cheaply, and in an interview a project you can show often counts for more than a module grade. What certificates do not deliver is mathematical depth and the academic degree that many pay scales, HR departments and visa procedures require. If you only need tools, a certificate gets you there faster. If you need the degree, there is no way around studying. The trade-off in detail is under professional development or a degree.

Vocational IT training is a formal qualification with documented learning outcomes, and that is exactly what credit transfer builds on. Universities typically credit introductory modules in programming, databases, operating systems and project work. An advanced vocational qualification often brings a larger block. For learning acquired outside higher education, an upper limit of 50 percent of a programme's ECTS usually applies.

What is almost never credited is maths and statistics. That is not obstruction, it is the academic backbone of the degree, and universities are reluctant to give it away. So plan conservatively. A bachelor covers 180 ECTS. If 30 are credited, 150 remain. At 15 ECTS per semester that is five years instead of six. Less spectacular than in other subjects, but reliable.

The order matters: clarify the credit first, then choose the university. With identical prior learning the differences between providers are substantial. How the process works is set out under crediting ECTS and credit for work experience. Which universities in the German-speaking region qualify is best compared on Hochschulnavigator.

An honest word on salary: the ranges in this field are wide, and the degree alone does not set them. What matters is sector, region, company size and above all whether you can show that your analyses led to decisions. A well documented project from your own job usually weighs more in an interview than the final grade. More on this under salary after your degree.

And the limit of the subject: data science does not open a door automatically. In small companies the role often does not exist as a separate position, and in regulated areas approval processes decide who may put models into production. Check the vacancies in your region before you enrol, and bring the options into an initial consultation.