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2019-08-21 CAS ADS M1 Data Acquisition and Management (3 days)

CAS Applied Data Science Module 1

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About Module Data Acquisition and Management
CAS Applied Data Science Module 1
 
See study plan on cas-applied-datascience.unibe.ch. 

Target group
  • Graduates and professionals enrolled for the CAS Applied Data Science 
Prerequisites 
  • University or University of Applied Sciences level degree (bachelor, master, phd) 
Methods
  • Lectures, tutorials, discussions, project work with written report
Practical information (time, location ...)
Time : 2019-08-21 - 23 09:00 - 17:00 (three full days)
Location : Mittelstrasse 43 , room 124, University of Bern

Language: English
Participants : Max 24
Registration : Mandatory (via Ilias or email to responsible)
Responsible : PD Dr. Sigve Haug
Schedule
Module 1 Data Acquisition and Management
 
Wednesday 2019-08-21 (Sigve Haug)
09:00 - 09:30 Introduction
09:30 - 10:30 About data and getting installed
11:00 - 12:30 Working with data (in Python)
12:30 - 13:30 Lunch
13:30 - 15:00 Infrastructures for data (with visit to a university data center)
15:30 - 17:00 Analysing data flows
 
Thursday 2019-08-22
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 - 14:15 Databases 1 (Kai Bruennler)
14:30 - 15:15 Databases 2 (Kai Bruennler)
15:30 - 16:15 Databases 3 (Kai Bruennler)

Friday 2019-08-23
09:00 - 10:30 Data management planning 1 (Jennifer Morger, Gero Schreier)
11:00 - 12:30 Data management planning 2 (Jennifer Morger, Gero Schreier)
12:30 - 13:30 Lunch
13:30 - 14:00 Discussion session on visualisation (Sigve Haug) 
14:00 - 15:00 Retrieving data from www 1 (Sigve Haug)
15:30 - 16:15 Retrieving data from www 2 (Sigve Haug
16:15 - 17:00 Project clarifications  (Sigve Haug)
17:00 - 18:00 Apero

Friday 2019-09-27 Submission deadline for project report 
Datasets
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 : https://archive.ics.uci.edu/ml/index.php
- or choose one from here : https://www.kdnuggets.com/datasets/index.html

Not all datasets are equally well suited for all things to learn and practice in the CAS. After day 3 in Module 1 you need to have choosen a dataset. 
Readings
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:
https://web.stanford.edu/class/e145/2007_fall/materials/noyce.html

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: https://jakevdp.github.io/PythonDataScienceHandbook

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. 
Lecturers and Coaches
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.

Nicole Kneubuehl and Jennifer Morger

Nicole and Jenny work as open access and research data management experts at the University Library, University of Bern.

Dr. Alexander Kashev

Alexander studied mathematics in Moscow and did his PhD in Computer Science at the University of Bern. He is now working for the Science IT Support unit at the Mathematical Institute.