194.048 Data-intensive Computing
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, VU, 2.0h, 3.0EC
TUWEL

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to

  • identify and solve practical problems of data-intensive computing
  • describe theoretical foundations of distributed data processing
  • apply methods for data processing in distributed data environments
  • apply machine learning to large-scale data in Hadoop/Spark-based cluster environments
  • design solutions for large-scale machine learning und data science problems and tasks
  • develop and deploy data-intensive edge computing strategies

Subject of course

Theory: Map/Reduce, Spark, edge computing, execution graphs
Practical part: Hadoop, Spark, implementation of large-scale data processing and machine learning tasks, edge computing

Teaching methods

Practical implementation of large-scale data processing and machine learning tasks in homogeneous and heterogeneous environments

Mode of examination

Immanent

Additional information


Vorbesprechung/First meeting: March 7th, Tuesday, 13:00-15:00

 

Location: EI 9 Hlawka HS at Gußhausstr. 27-29 - Neu El (ground floor)

 

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue13:00 - 15:0007.03.2023 - 20.06.2023EI 9 Hlawka HS - ETIT Lecture
Data-intensive Computing - Single appointments
DayDateTimeLocationDescription
Tue07.03.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture
Tue14.03.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture
Tue28.03.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture
Tue18.04.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture
Tue06.06.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture
Tue13.06.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture
Tue20.06.202313:00 - 15:00EI 9 Hlawka HS - ETIT Lecture

Examination modalities

Grading based on 3 assignments (A1: 100pt, A2: 100pt, A3: 100pt; Sum 300pt);

ECTS Breakdown:
3.0EC = 75h
7.5 lectures:     15h
Exercise 1:  20h
Exercise 2:  20h
Exercise 3:  20h

Course registration

Begin End Deregistration end
03.02.2023 00:00 28.03.2023 23:59 23.03.2023 23:59

Precondition

The student has to be enrolled for at least one of the studies listed below

Curricula

Study CodeObligationSemesterPrecon.Info
066 645 Data Science Mandatory2. Semester

Literature

No lecture notes are available.

Miscellaneous

  • Attendance Required!

Language

English