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2022-08-24 - 26 CAS ADS M1 Data Acquisition and Management (3 days)

CAS Applied Data Science Module 1


CAS Applied Data Science Module 1
See study plan on 

Target group
  • Graduates and professionals enrolled for the CAS Applied Data Science 
  • University or University of Applied Sciences level degree (bachelor, master, phd) 
  • Lectures, tutorials, discussions, project work with written report
Time : 2022-08-24 - 26 09:00 - 17:00 (three full days)
Location : University of Bern, Main Building, room 106. 
URL: Login to see the link for remote participatoin.

Language: English
Participants : Max 24
Registration : Mandatory (via Ilias or email to responsible)
Responsible : PD Dr. Sigve Haug
Module 1 Data Acquisition and Management

Wednesday 2022-08-24 (Sigve Haug)
09:00 - 09:30 Welcome and introduction
09:30 - 10:00 About data and data science
10:00 - 10:30 Data management with Pandas
10:30 - 11:00 Break
11:00 - 12:30 Data management with Python and Pandas
12:30 - 13:30 Lunch
13:30 - 15:00 Infrastructures for data
15:30 - 17:00 Analysing data flows
Thursday 2022-08-25
09:00 - 09:30 Dicsussion session
09:30 - 10:30 Data visualisation 1 (Sigve Haug)
11:00 - 12:30 Data visualisation 2 (Sigve Haug)
12:30 - 13:30 Lunch
13:30 - 15:00 Webscraping (Sigve Haug)  
15:30 - 17:00 APIs (Sigve Haug) 

Friday 2022-08-26

09:00 - 12:30 Databases 1 (Kai Bruennler)
12:30 - 13:30 Lunch
13:30 - 15:00 (Database) Code Understanding, installations, trouble shooting (Sigve Haug)
15:00 - 15:30 Break
15:30 - 16:00 Trouble shooting (Sigve Haug)
16:00 - 16:30 Project clarifications (Sigve Haug)
16:30 - 16:45 Feedback
17:00 - 1X:00 Apero

Friday 2022-09-02 Graduation apero and event at 17:00. You are also very welcome !
Friday 2022-10-31 Submission deadline for Conceptual Design Report. Please upload to folder here on Ilias.
During the CAS you ideally work on one or two datasets during all module works. You can

- bring your own dataset from research or work or private project
- or choose one from here :
- or choose one from here :
- or choose one from any other source you fancy
Not all datasets are equally well suited for all things to learn and practice in the CAS. 
You are not supposed to read many books during this CAS, but of course you can. What you need to consult all the time is online documentation on all topics, google brings you there - and wikipedia of course.

To warm up a bit, you may read Tom Wolfe's account on the rise of the Silicon Valley:

The technical (Python) skills being introduced in Module 1 and practiced in the other modules are covered in the four first chapters of the Python Data Science Handbook:

Please work through this notebook to check or practice your Python skills. By the begin of Module 3 we expect you to master that notebook.

What you may want to read or at least have in the shelf, is some reference work on Applied Statistics and Machine Learning. In module 2 and 3 these will come. 
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. 

Prof. Dr. Kai Bruennler

Kai is currently professor at the University of Applied Sciences Bern. In addition to databases he has interests in topics like blockchain, crypto currencies etc. He has previously been working at the University and industry.