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Data science is the discipline of deriving knowledge from large and complex datasets. It combines statistics, computer science, mathematics, and often domain-specific knowledge from a particular field of research to identify patterns, understand relationships, and make predictions.
At its core, it is about transforming data into insights. To do this, data science makes use of modern tools such as machine learning, artificial intelligence (AI), data visualization, and distributed computing infrastructures. Researchers work with structured data (e.g., time series, laboratory data) as well as unstructured data (e.g., text, images, audio, or video files).

Why is data science so important in research?

• Growing volumes of data
  Today, enormous amounts of data are generated in nearly all scientific disciplines—whether through high-throughput experiments in biology, sensor data in environmental research, or simulations in physics. Without data science, this data could not be fully analyzed and utilized.
  
  
• New insights from existing data
  Data science makes it possible to extract valuable information from existing data by recombining, filtering, or modeling it. This allows hypotheses to be tested and even entirely new research questions to be formulated.
  
  
• Efficiency and automation
  Machine learning methods can be used to automate complex analyses. This not only saves time but also makes it possible to process data sets that would simply be too large for humans to handle.
  
• Better predictions
  In climate research, epidemiology, or materials science, data-driven models provide reliable forecasts that help make decisions based on sound evidence.
  Interdisciplinarity