Data March

DATA MARCH
Intensive course in data science and machine learning for students across all disciplines
We live in a world of data. Whether you are studying biochemistry or physics, art history or medicine, business studies or law: Data is generated in all subject areas and needs to be tamed.
This is where data science comes into play: In our intensive course "Data March," you will learn the basics of “taming" data and be introduced to the world of data analysis in a practical way. You will evaluate and interpret data sets and develop a deeper understanding of what data really means.
Whether for your studies or your professional future, this knowledge will give you a decisive advantage. Data Science strengthens your profile and opens up new opportunities (opens in a new window). (This PDF is not accessible) for you.

What is the Data March?
The “Data March” is a course for newcomers to the world of data science and machine learning. Every year in March, you can expect four weeks full of practical projects and exciting content that can be perfectly integrated into the curriculum of your degree program.
And the best thing is: You decide how much you want to get involved. One, two, three, or four weeks - and receive 2, 4, 6, or 8 ECTS credits depending on how many weeks you choose to participate.
Why should I participate?
Career boost - Data science skills are more in demand than ever and open up numerous career opportunities (opens in a new window). (This PDF is not accessible) for you.
Practical relevance - Apply your knowledge in numerous small projects.
Flexibility - Decide for yourself how deep you want to delve into the subject matter.
Networking - Learn together with students from different subject areas.
Key data
?? | Teaching format
Intensive course |
?| Venue
In-class course in lecture hall H401 (external link, opens in a new window) (Bajuwarenstr. 4) at the University of Regensburg |
? | ECTS points
2 per week, i.e., 2, 4, 6 or 8 depending on attendance |
? | Prior knowledge
No prior knowledge required. A-level knowledge is sufficient. |
? | Enrollment
Via the SPUR (external link, opens in a new window) campus portal |
? | Dates
March 3–28, 2025 Mon.–Fri. from 8 am – 12 pm c.t. and 2 pm – 6 pm c.t. |
? | Duration
Depending on interest 1, 2, 3 or 4 weeks |
? | Teaching language
English |
? | Number of participants
80 places, available on a first-come-first-served basis |
? | Exam
Written exam consisting of up to four 30-minute examination units (each covering the content of one of the four weekly blocks). The four examination units can be taken independently of each other. |
Program
The individual weeks build on each other, but can also be completed separately if you have the relevant prior knowledge.

Week 1:
Coding - Learn the basics of the Python programming language and how to apply it in data science.
Lecturer: Prof Dr Rainer Spang

Week 2:
Sampling - Understand the essentials and use them directly in practical projects.
Lecturer: Prof Dr Florian Erhard

Week 3:
Inference - Dive into the world of data analysis and learn important techniques, such as exploration, hypothesis testing, and regression.
Lecturer: Prof Dr Thomas Jaki

Week 4:
Machine Learning - Discover how machines learn and apply this knowledge in exciting projects.
Lecturer: Prof Dr Merle Behr