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.

2019S, VU, 2.0h, 3.0EC
TUWEL

Properties

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

Aim of course

  • Identifying and solving practical problems of data-intensive computing
  • Describing theoretical foundations of distributed data processing
  • Application of methods for data processing in distributed data environments
  • Application of machine learning to large-scale data in Hadoop/Spark-based cluster environments

Subject of course

Theory: Hadoop, Spark
Practical part: implementation of large-scale data processing and machine learning tasks

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Thu14:00 - 16:0007.03.2019 - 27.06.2019FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu14:00 - 16:0004.04.2019 - 06.06.2019EI 3 Sahulka HS - UIW Lecture
Thu09:00 - 19:0027.06.2019FAV Hörsaal 1 Helmut Veith - INF Presentations
Data-intensive Computing - Single appointments
DayDateTimeLocationDescription
Thu07.03.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu14.03.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu21.03.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu28.03.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu04.04.201914:00 - 16:00EI 3 Sahulka HS - UIW Lecture
Thu11.04.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu02.05.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu09.05.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu16.05.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu23.05.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu06.06.201914:00 - 16:00EI 3 Sahulka HS - UIW Lecture
Thu13.06.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Lecture
Thu27.06.201909:00 - 19:00FAV Hörsaal 1 Helmut Veith - INF Presentations
Thu27.06.201914:00 - 16:00FAV Hörsaal 1 Helmut Veith - INF Presentations

Examination modalities

Grading based on 3 assignments (A1: 20pt, A2: 30pt, A3: 50pt; Sum 100pt; A1+A2 >= 35pt, to be eligible for A3!); presence and contributions in lectures mandatory!

ECTS Breakdown:
3.0EC = 75h
6 lectures:        12h
Assignment 1:  12h
Assignment 2:  17h
Assignment 3:  30h
Present + Prep.: 4h

Course registration

Begin End Deregistration end
01.02.2019 00:00 10.03.2019 23:59 10.03.2019 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