052813 VU Scientific Data Management (2023S)

This course will be taught in English and take place on-site. The lectures will be streamed, recorded, and made available on Moodle.

The course introduces central methods for the organization and analysis of large and scientific data such as distributed data repositories, index data structures, hashing, classification, and clustering techniques. Specific methods for structured data such as sets, images, text documents, and graphs are discussed.

The lectures are complemented by exercises and programming assignments. Students will learn ways to realize similarity search and data mining on large data, e.g., using parallelisation with MapReduce, Apache Spark, or filter-refinement techniques.

Subject-specific goals:
- Analysis of scientific data
- Interpretation and evaluation of results of the analysis process
- Choosing and applying techniques for structured data
- Implementation of scalable solutions for large amounts of data
- Support and advice of users

Generic goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in data mining and scientific computing