Symbolic image for data science with ones and zeros

Data Literacy

Data Literacy - Introduction to Data Science

Transdisciplinary course in the field of study Digitalization and Society - creditable as a compulsory elective subject (if possible) or for a certificate

Whether it is the interpretation of number-based news reports, data-supported laboratory experiments or the handling of shares and finances: data analysis is the tool for the correct handling of information. It is therefore important to develop a basic understanding of data and the possibilities associated with it, i.e. data literacy. This course is aimed at students, regardless of specialization, who want to understand the first basic methods of data science. Data literacy includes both the competence of basic data analysis including representation and communication as well as reading and interpreting data correctly.

Therefore, we want to bring data literacy skills to every student without formulas and proofs, but through a lecture and hands-on projects with intensive supervision. No prior knowledge of statistics or programming experience is required, an introduction to Python for data analysis will be given as part of the course.

The Data Literacy course has already had four successful runs and will be offered again in the winter semester 2025/26. The venue is the high-rise building (C10) of the Schöfferstraße campus and it is offered by the Department of Mathematics and Natural Sciences and the Department of Computer Science. We look forward to your participation.

    Aim of the course

    • Introduction to Python programming
    • Basic data literacy skills
    • Data analysis through programming
    • Deeper understanding of data analysis through application in various specialist areas

    Target group

    Students of all disciplines who are interested in the topic of data and are willing to learn the basics of data analysis with the help of Python programming.

    What requirements do I need to meet?

    Only school mathematics is required; prior knowledge of statistics and programming is not necessary.

    How do I register and where does the event take place?

    Times and room will be announced in the Moodle course before the start of the lecture. Registration for the course takes place via self-enrollment in the Moodle course.

    What form does the examination take?

    Evaluation of a data set including documentation and presentation.

    Who can help me with questions about the event?

    Alexander Siebert alexander.siebert@h-da.de