Universität Wien

053614 VU Statistics for Data Science (2022W)

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

There will be no course on 11.11.2022.

  • Friday 07.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 14.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 21.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 28.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 04.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 11.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 18.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 25.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 02.12. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 09.12. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 16.12. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 13.01. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 20.01. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 27.01. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

The goal of the course is to establish a thorough understanding of basic concepts and methods of statistical inference in the context of modern data science.

We will cover the following topics:
- Statistical inference vs. statistical learning
- Bootstrap and Jackknife methods
- Linear Models and High-dimensional data
- Statistical inference for network data
- Differential Privacy

This course is divided into lectures and lab/homework sessions.

Assessment and permitted materials

Students have to solve homework problems and present their results in the lab session.
At the end of the semester students can choose the format of their final exam: a) oral final exam or b) take-home project.
In case a) you get 30 minutes of general questions about the course material. In case b) you have to do a 15 minutes discussion of your solutions with the lecturer.

Minimum requirements and assessment criteria

Homework 60%
Final Exam 40%

At least half of the homework problems have to be solved in order to get a passing grade.

Examination topics

Option a)
The final exam will cover all the material that was discussed in lectures and homework sessions during the semester.

Option b)
The final project will require you to independently solve theoretical and applied statistics problems related to what we have learned in the course.

Reading list

Rice, J.A. (2007): “Mathematical Statistics and Data Analysis”, Duxbury.

Association in the course directory

Modul: SDS

Last modified: Mo 31.10.2022 13:28