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Praktikum Big Data Science im SS 2017

im Rahmen des ZD.B Innovationslabors Big Data Science
Prof. Dr. Bernd Bischl, Prof. Dr. Dieter Kranzlmüller, Prof. Dr. Thomas Seidl



News

  • The slides for the second plenum session are now online.
  • The kickoff-meeting slides are now online.
  • The kickoff-meeting will take place on the 03.05.2017, 14:00 - 16:00, in Room 157 (Oettingenstr. 67).
  • Registration is now closed, applicants have been notified via e-mail.
  • Registration in UniWorX is now open. Slots will be assigned primarily based on the information provided in the application process, to ensure that the eligiblility criteria will be met by the majority of the participants.

Audience

  • Master students in Informatics, Bioinformatics, Mediainformatics, Statistics and Data Science.

Content

Big Data and Data Science gain increasing attention and significance, as they are discovered by scientific and economic domains. Today Data Science and Big Data advance into various facets of our daily life. The purpose of this practical course is to make the students familiar with the practical approach of Big Data applications and Data Science. By learning the handling with state-of-the-art Big Data tools the core concepts of dealing with data streams and the Big Data process are conveyed.

Besides the aspects of data-driven analysis, this course aims to foster the practice of agile project management methods and the application of software engineering techniques. In particular, the students will implement a data driven system based on the cloud processing platform Apache Flink. Based on this platform, the system will incorporate the complete process of knowledge discovery including data preprocessing, data analysis, evaluation and processing analysis results.

The practical course is introduced with a kick-off meeting at the start of the semester.

Furthermore, this course targets the responsible and efficient handling of limited resources.

The lab course is funded by the ZD.B Innovationslabor Big Data Science.



Eligibility Requirements

As the lab course will cover several advanced topics in data science and big data analytics, successful participation in at least one of the following lectures or similar prior experience is recommended:

  • Knowledge Discovery in Databases I
  • Knowledge Discovery in Databases II
  • Big Data Management and Analytics
  • Machine Learning

The lab course requires skills and experience in programming and software engineering.


Organization


Time and Location

All time specifications are sine tempore (s.t.).

Event Date Time Location Material
Kickoff Meeting 03.05.2017 14:00 - 16:00 Room 157 (Oettingenstr. 67) Slides Topics
Plenum Session 10.05.2017 14:00 - 15:00 Room 157 (Oettingenstr. 67) Slides Git Demo
Plenum Session 24.05.2017 16:00 - 17:00 Room 157 (Oettingenstr. 67)
Plenum Session 07.06.2017 16:00 - 17:00 Room 157 (Oettingenstr. 67)
Plenum Session 21.06.2017 16:00 - 17:00 Room 157 (Oettingenstr. 67)




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