Data science and big data ST-LE-2026-01

by Dr Björn Tings (Deutsches Zentrum für Luft- und Raumfahrt)

Thursday Feb 12, 2026, 9:00 AM → 12:00 PM Europe/Berlin

Room 2.2070/2.2090, 2nd floor + Zoom (hybrid)

Motivation

Parallel to the digital transformation, a novel scientific discipline has been developed – data science. Data science allows new approaches for interdisciplinary (big) data analyses through complex algorithms and artificial intelligence (machine learning, deep learning etc.). Such approaches extract information from the data sets beyond the current scientific knowledge. Therefore, data science is of interest for nearly all research as well as industry/economy fields and often termed as a novel key discipline (e.g. Society of Informatics e.V., 2019).
This course provides a basic overview about data science applications. To produce reliable data science results a profound knowledge about the data analyses methods, data management techniques and innovative technologies is required. Additionally, to assess these results and approaches an awareness of their ethical, legal, and social implications is demanded (all topics are addressed in the following courses and operator tracks).

Learning Contents

1. History 
   - Milestone of AI
   - Timeline comparison with CPU power and storage costs
2. Clarification of terms
   - Statistics ⊇ Machine Learning ⊇ Deep Learning
   - Data Mining ⊇ Big Data
   - Machine Learning vs. Artificial Intelligence
3. What is Data Science?
   - Collection -> Analysis -> Visualization
   - Machine Learning
          o Supervised Learning
          o Unsupervised Learning
          o Reinforcement Learning
4. What is Big Data
   - Five Vs Model
   - Privacy
5. Tools

Learning Outcomes

Basic overview about data science applications, methods, terms, tools, and big data.

Prior knowledge

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Further reading

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Lecture Materials

Zoom Link