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050038 VU Scientific Data Management (2016S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 01.02.2016 09:00 to Mo 22.02.2016 23:59
- Deregistration possible until Su 20.03.2016 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Friday 04.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 18.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 08.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 15.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 22.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 29.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 06.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 13.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
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Friday
20.05.
09:45 - 13:00
Hörsaal 2, Währinger Straße 29 2.OG
Seminarraum 3, Währinger Straße 29 1.UG
Seminarraum 4, Währinger Straße 29 1.UG - Friday 27.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 03.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 10.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 17.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 24.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Information
Aims, contents and method of the course
The lecture covers essential methods for organizing and analyzing large scale scientific data: distributed data repositories, index structures, hashing and clustering methods. In programming exercises the participants experience possibilities to support similarity search and data mining with parallelization (MapReduce, SPARK) and filter-refinement techniques.
Assessment and permitted materials
Active participation
exercises and programming exercises
final test
exercises and programming exercises
final test
Minimum requirements and assessment criteria
Course-specific goals:
- analyze and interpret scientific data,
- evaluate the results,
- select, design and implement solutions for big data,
- support users.General goals:
- experience of working in a team,
- improvement of implementation skills,
- insights into the links between data mining and scientific computing.
- analyze and interpret scientific data,
- evaluate the results,
- select, design and implement solutions for big data,
- support users.General goals:
- experience of working in a team,
- improvement of implementation skills,
- insights into the links between data mining and scientific computing.
Examination topics
Exercises
programming exercises in teams
final test
programming exercises in teams
final test
Reading list
Ester M., Sander J. Knowledge Discovery in Databases: Techniken und Anwendungen.
J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets.
J. Han, M. Kamber, J.Pei.Data Mining: Concepts and Techniques.
I. H. Witten , E. Frank, M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques.
J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets.
J. Han, M. Kamber, J.Pei.Data Mining: Concepts and Techniques.
I. H. Witten , E. Frank, M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques.
Association in the course directory
Last modified: Mo 07.09.2020 15:29