Digitization and Data Analytics: Architectures, Methods, and Consequences - Summer Semester 2026

TU Dresden | Sommersemester 2026 Digitization and Data Analytics: Architectures, Methods, and Consequences - Summer Semester 2026

Lehrbeauftragter:Prof. Wolfgang E. Nagel

 

 

Lecture:Wednesday, 2. DS (09:20 - 10:50 Uhr), WIL/A317

 

Exercise:Thursday, 3. DS (11:10 - 12:40 Uhr), WIL/A317 (bring your own computer and be ready to connect to the HPC system)


The course "Digitization and Data Analytics, Architectures, Methods, and Consequences" provides an overview and special knowledge about current approaches for the analysis of large amounts of data (Big Data). This covers different Data Analytics approaches and their application in the High-Performance Computing (HPC) environment, specific computer architectures as well as data processing in the HPC environment. In addition, data analytics frameworks and selected applications are presented.
The aim of the lecture is to show the interlocking of concepts and methods from the field of HPC and from Data Analytics with regard to the rapidly advancing digitalization, to present the necessary tools in practical exercises and to discuss the consequences of these developments.

Lecture Schedule

  • 15.04: Lecture 1: Introduction
  • 22.04: Lecture 2: Data Preprocessing
  • 29.04: Lecture 3: Basics of Statistics
  • 06.05: Lecture 4: Research Data Management
  • 13.05: Lecture 5: Machine Learning I (as online video)
  • 20.05: Lecture 6: Machine Learning II (as online video)
  • 27.05: Pentecost
  • 03.06: Lecture 7: Machine Learning III
  • 10.06: Lecture 8: Deep Learning I (as online video)
  • 17.06: Dies Academicus
  • 24.06: Lecture 9: Deep Learning II
  • 01.07: Lecture 10: Distributed Computing I
  • 08.07: Lecture 11: Distributed Computing II
  • 15.07: Consequences
  • 22.07. flexible date, depending on schedule for written exam

Exercise Schedule:

  •  23.04: Exercise 1 - Introduction
  • 30.04: Self study – no exercise
  • 07.05: Exercise 2 - Preprocessing
  • 14.05:Ascension Day
  • 21.05: Exercise 3 - Basics of Statistics
  • 28.05: Pentecost
  • 04.06: Exercise 4 - Machine Learning I
  • 11.06: Exercise 5 - Machine Learning II
  • 18.06: Exercise 6 - Machine Learning III
  • 25.06: Exercise 7 - Machine Learning IV
  • 02.07: Exercise 8 - Big Data I
  • 09.07: Exercise 9 - Big Data II
  • 16.07: Exercise 10 - Big Data III
  • 23.07: flexible date, depending on schedule for written exam

Information on written exam: TBD
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