Universität Wien
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053614 VU Statistics for Data Science (2020W)

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

  • Thursday 01.10. 13:15 - 16:30 Digital
  • Wednesday 07.10. 09:45 - 11:15 Digital
  • Wednesday 14.10. 09:45 - 11:15 Digital
  • Thursday 15.10. 13:15 - 16:30 Digital
  • Wednesday 21.10. 09:45 - 11:15 Digital
  • Wednesday 28.10. 09:45 - 11:15 Digital
  • Thursday 29.10. 13:15 - 16:30 Digital
  • Wednesday 04.11. 09:45 - 11:15 Digital
  • Wednesday 11.11. 09:45 - 11:15 Digital
  • Thursday 12.11. 13:15 - 16:30 Digital
  • Wednesday 18.11. 09:45 - 11:15 Digital
  • Wednesday 25.11. 09:45 - 11:15 Digital
  • Thursday 26.11. 13:15 - 16:30 Digital
  • Wednesday 02.12. 09:45 - 11:15 Digital
  • Wednesday 09.12. 09:45 - 11:15 Digital
  • Thursday 10.12. 13:15 - 16:30 Digital
  • Wednesday 16.12. 09:45 - 11:15 Digital
  • Thursday 07.01. 13:15 - 16:30 Digital
  • Wednesday 13.01. 09:45 - 11:15 Digital
  • Wednesday 20.01. 09:45 - 11:15 Digital
  • Thursday 21.01. 13:15 - 16:30 Digital
  • Wednesday 27.01. 09:45 - 11:15 Digital

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.

Depending on our progress and the prior knowledge of students, we will cover some of the following topics:
- Statistical inference vs. statistical learning
- Bootstrap and Jackknife methods
- Linear models and causal inference
- Statistical inference for network data
- High-dimensional data and inference post-model-selection
- Differential Privacy
- Bayesian statistics and MCMC methods

This course will be held 100% online, with the exception of the possibility to individually visit the office hours of the lecturer by appointment. There will be recorded lectures plus interactive discussion and exercise sessions.
Please closely follow the Moodle course webpage!

Assessment and permitted materials

Student have to solve homework problems and present their results in an interactive online class.
There will be a written final exam.

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

The final exam will cover all the material that was discussed in lectures and homework sessions during the semester.
You only need to read the designated sections of the textbook.

Reading list

Wasserman, L. (2003): "All of Statistics: A Concise Course in Statistical Inference", Springer.

Association in the course directory

Modul: SDS

Last modified: Fr 12.05.2023 00:13