Universität Wien

052113 VU Software Tools for Computational and Data Science (2022S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work

This course is equivalent to the VU "Software Tools and Libraries for Scientific Computing"
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 02.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 07.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 09.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 14.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 16.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 21.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 23.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 28.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 30.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 04.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 06.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 25.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 27.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 02.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 04.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 09.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 11.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 16.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 18.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 23.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 25.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 30.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 01.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 08.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 13.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 15.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 20.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 22.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 27.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 29.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

We discuss software tools for computational and data science including their foundational numerical algorithms. The students get familiar with using important computational software like BLAS, LAPACK, MLPACK, GNU Octave, MPFR, and PETSc. Computational results are evaluated to ensure good performance. Hands-on experience is strengthened by in-depth discussions.

The students are expected to have good general programming skills, basic familiarity with programming in C (other languages upon request), and experience in using GNU/Linux and Bash. The course builds upon the contents of the modules "Introduction to Numerical Computing" (NUM) and "Combinatorial and Numerical Algorithms" (CNA).

Assessment and permitted materials

The grading will be based on homeworks, two written exams (closed book), and two small projects. The projects shall be chosen with respect to the discussed topics.

Minimum requirements and assessment criteria

Presence is mandatory during the entire course. Each part (homework and projects with oral discussion on the computer, written exam) needs a score of at least 50%; grading (20% homework, 40% exams, 40%project): <50%=5, 50% up to 62.5%=4, up to 75%=3, up to 87,5%=2, 87,5% or better=1.

Examination topics

All topics of the lectures will be relevant for the exams.

Reading list

The lectures are accompanied by slides which point to additional relevant literature (supplied in the course). Textbooks etc. are not required.

Association in the course directory

Module: STL

Last modified: Tu 01.03.2022 18:48