Universität Wien

053614 VU Statistics for Data Science (2021W)

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

  • Friday 01.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 08.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 15.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 22.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 29.10. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 05.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 12.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 19.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 26.11. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 03.12. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 10.12. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 17.12. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 07.01. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 14.01. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 21.01. 15:00 - 18:15 Seminarraum 6, Währinger Straße 29 1.OG
  • Friday 28.01. 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
- High-dimensional data and inference post-model-selection
- Statistical inference for network data
- Differential Privacy

We will try to do this course in physical presence in the lecture hall if possible. Please closely follow the Moodle course webpage to stay up to date! (coming soon)

Assessment and permitted materials

Student have to solve homework problems and present their results in an interactive online class.
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 completed 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: Tu 28.09.2021 13:28