052813 VU Scientific Data Management (2024S)

This course will be taught in English and take place on-site. The lectures will be streamed, recorded, and made available on Moodle. Exercise sessions will be on-site only and attending them is mandatory. The midterm and final exams will be on-site.

The course introduces central methods for organizing and analyzing large and scientific data, such as distributed data repositories, index data structures, hashing, classification, and clustering techniques. In particular, methods for structured data such as sets, images, text documents, and graphs are discussed.

Exercises and programming assignments complement the lectures. Students will learn ways to realize similarity search and data mining on large data, e.g., using parallelization 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

It is recommended to complete the following courses before attending:
- Algorithmen und Datenstrukturen
- Datenbanksysteme
- Software Engineering
- Netzwerktechnologien