photograph of a laptop computer



Curriculum: Starter Track

Data science and big data


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 (timeline comparison with CPU power and storage costs) & clarification of terms
- Statistics > Machine Learning > Deep Learning
Data Mining > Big Data
- Machine Learning vs. Artificial Intelligence

2. What is Data Science?
- Collection > Analysis > Visualization

- Machine Learning

   - Supervised Learning

   - Unsupervised Learning

   - Reinforcement Learning
- Big Data (data science with huge datasets, more memory of one PC required)
   - Five Vs Model

   - Privacy

3. Tools

Learning Outcomes

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

Prior knowledge and requirements


Further reading



February 2, 2023, 9:30 AM - 11:30 AM


Haus 2 (Oxford)
Large Seminar Room 2.2090, 2nd floor

Mary-Somerville-Str. 2
28359 Bremen

Online via Zoom

Tings, Dr. Björn

German Aerospace Center
Remote Sensing Technology Institute
Researcher in working group Synthetic Aperture Radar (SAR) oceanography


Lecturers information