Symbol Kurs

2021-10-18 ~ 2021-11-01 Introduction to Statistical Data Analysis with Python ( free, only for UNIBE postdocs and phd students)

Three days on how to perform statistics with Python.


Statistical data analysis with Python

Statistical inference is the process of deducing properties of an underlying probability distribution (model/theory) by analysis of data. What does it mean when we quantify new measurements, claim findings like discoveries or exclusions of new phenomena? Extracting knowledge from data is about probability and uncertainties.

Learning outcomes
  • tbc 
Target group
  • PHD Students, UNIBE Postdocs
  • Basic programming skills are necessary, we don't reserve time for basic programming concepts. If you don't have these, you may take the course Introduction to Python programming in advance. 
  • Course languages are Python and English
  • Short theory sessions followed by hands-on tutorials with Jupyter notebooks 
Certificate and points
  • tbc
  • The coaches are local and external experts
Time : 2021-10-18, 2021-10-25, 2021-11-01 time 09:00-17:00
Location : University of Bern, Hochschulstrasse 4, Room 304. The course will be transmitted online in any case, but we have also reserved a room at the university for on-site attendance (see "Practical information" on Ilias). The university's corona policy requires participants to have a Covid certificate. This means for this course, if you want to come on site, you must have a certificate that you are tested, recovered or vaccinated. So we are happy if you comply if you like to come on site.

Training language: English
Participants : Max 24
Registration : Mandatory (use the Join button, one has to be logged into Ilias, button in the upper right corner)
Coaches : PD Dr. Sigve Haug (responsible)
Fee: Free of charge
Certificate: Certificate of Attendance
PD Dr. Sigve Haug

Sigve studied physics in Germany, Spain and Norway. He has been involved in neutrino physics experiments and high energy frontier experiments, often with main focus on the computing challenges related to the large and distributed data from these experiments. Today he is working for the Albert Einstein Center for fundamental Physics and the Mathematical Institute of the University of Bern where is leading the Science IT Support unit (