In the field of data science, bioinformatics focuses in particular on microbiology, genetics, and the analysis of biological sequences.
To evaluate the huge volumes of data generated by experiments, statistical models and machine learning are used. Parallel processing and efficient algorithms make it possible to analyse these data faster and more easily.
Data science therefore helps bioinformatics achieve results more quickly and with less effort.

In business information systems, data science is used to evaluate and analyse large data sets, for example statistical data.
Methods from machine learning are often used to create forecasts. One current research example is the analysis of outrage waves on social networks. The goal is to identify the factors that characterise such waves.
By analysing these factors automatically, future systems should be able to detect such developments before they spread widely, enabling early countermeasures.
Data science in business information systems thus contributes to making online interaction fairer and more manageable.

In medical informatics, methods from data science are used, among other things, to evaluate clinical studies.
Different kinds of data are brought together and prepared for analysis, for example by checking them for plausibility.
In the long term, data science in medical informatics is also intended to derive predictions from the results obtained and could therefore help revolutionise medical research in the future.

Digital Humanities is a very broad research field that, in the widest sense, deals with digitisation and its methods for the humanities.
Here too, data science is used to analyse existing research data, for example in the analysis of old religious texts. At the same time, new methods are being developed, such as the design of virtual museum exhibitions or new ways of presenting research results.
Another example is archaeology, where historical sites are reconstructed or missing parts of sculptures are digitally restored.
Data science therefore also offers the humanities a wide range of applications and new ways to analyse existing data.

Breeding Informatics focuses on making big data usable for improving animal and plant breeding and related fields.

For example, genomic variants responsible for desirable animal or plant traits are filtered out from sequence data sets that may comprise several hundred gigabytes. This knowledge can then be used in breeding programmes to identify the most promising individuals for reproduction.
In addition, machine learning methods are used to detect anomalous behaviour in animal populations in real time from camera image sequences so that countermeasures can be taken.

In precision farming, for example, the right amount of fertiliser is determined and applied individually for each plant. The consequences of climate change for local animal and plant diversity are also assessed and corresponding countermeasures are proposed.

Breeding Informatics therefore helps ensure that we can feed more and more people in the future while also practising agriculture more sustainably.

Physics is a diverse field of research whose extensive, and often enormous, experimental data sets are evaluated with the help of advanced data science methods. In particular, machine learning, statistical methods, and time-series analysis are used for this purpose, while image recognition methods are also becoming increasingly important in areas such as materials physics and biophysics. The term "big data" therefore applies to physics just as much as to many other disciplines.

Within physics, the application area of data science spans a broad spectrum, from the study of fundamental particles to observations of the visible universe. This application area is therefore intended to show how data science methods are used to understand our world and our universe more deeply.

This application area focuses on the analysis of data from geology, forestry, and agricultural sciences. The emphasis lies on climate data and data that describe ecosystems and their condition. With the help of data science, this makes it possible, for example, to develop models that predict CO2 emissions or to analyse satellite images with regard to landscape characteristics.

Computational Neuroscience combines neuroscience with mathematical models and data science in order to understand how the brain works. Modern measurement techniques such as calcium imaging or Neuropixels electrodes generate huge amounts of data from thousands of neurons at the same time, a challenge that would be impossible to handle without data science.

Machine learning methods, especially deep learning, are used to analyse neural activity patterns and predict how the brain processes sensory information. For example, models can be trained to reconstruct which image an animal is currently seeing from the activity of visual brain areas. Conversely, artificial neural networks help to develop and test hypotheses about biological principles of computation.

Applications range from basic research, such as understanding vision, smell, or motor control, to clinical questions such as the development of brain-computer interfaces or the analysis of neurological diseases. In Computational Neuroscience, data science helps us better understand one of the most complex systems in nature, the brain, and to make that knowledge usable for medical and technological innovation.