Praktikum Big Data Science (SS 2018)
im Rahmen des ZD.B Innovationslabors Big Data Science
Prof. Dr. Bernd Bischl, Prof. Dr. Dieter Kranzlmüller, Prof. Dr. Thomas Seidl
Please reserve resources here
- Volume: 4 hours weekly (equals 12 ECTS)
- Lecture: Prof. Dr. Thomas Seidl
- Assistants: Max Berrendorf, Felix Borutta, Evgeniy Faerman
- Audience: The course is directed towards master students in Informatics, Media Informatics, Statistics and Data Science
- Registration: Please register in uniworx (check Eligibility Requirements for the information how to apply)
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.
Please explain how you meet the requirements and describe relevant practical experiences in your uniworx application!
Time and Locations
All times are s.t. (sine tempore)
|Kick-Off Meeting||12.04.2018||14:00 - 18:00||U 151||Slides|
|Plenum Session||19.04.2018||14:00 - 18:00||U 151|
|Plenum Session||03.05.2018||14:00 - 18:00||U 151|
|Plenum Session||17.05.2018||14:00 - 18:00||U 151|
|Fronleichnam (No Plenum Session)||31.05.2018|
|Plenum Session||14.06.2018||14:00 - 18:00||U 151|
|Plenum Session||28.06.2018||14:00 - 18:00||U 151|
|Final Presentations||12.07.2018||14:00 - 18:00||U 151|
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 the Big Data process are conveyed.
In particular, the students will work on Deep Learning approaches in various different data domains, including image, text, point clouds and graphs. 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. Furthermore, this course targets the responsible and efficient handling of limited resources.
The practical course is introduced with a kick-off meeting at the start of the semester.
The lab course is funded by the ZD.B Innovationslabor Big Data Science.