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2019-08-27 CAS ADS M2 Statistical Inference for Data Science (4 days)

CAS Applied Data Science Module 2

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About Module Data Acquisition and Management
CAS Applied Data Science Module 2
 
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 poster presentation
Practical information (time, location ...)
Time : 2019-09-13 - 30 09:00 - 17:00 (afternoons for self studies) 
Location : Mittelstrasse 43, room 124, University of Bern 

Location 2018-09-13 (presentation day): Room 105, Main Building , University of Bern

Language: English
Participants : Max 24
Registration : Mandatory (via Ilias or email to responsible)
Lecturer: Prof. Dr. Geraldine Conti
Responsible : PD Dr. Sigve Haug
Schedule
Module 2 Statistical Inference for Data Science
 
Tuesday 2019-08-27
09:00 - 10:30 Introduction, Descriptive statistics
10:30 - 11:00 Coffee Break
11:00 - 12:30 Notebook 1 demo
12:30 - 13:30 Lunch
13:30 - 17:00 Self study Notebook 2
 
Wednesday 2019-08-28
09:00 - 10:30 Discussion session
10:30 - 11:00 Coffee Break
11:00 - 12:30 Parameter estimation, work on Notebook 3
12:30 - 13:30 Lunch
13:30 - 17:00 Self study

Thursday 2019-08-29
09:00 - 10:30 Discussion session
10:30 - 11:00 Coffee Break
11:00 - 12:30 Hypothesis testing, work on Notebook 4
12:30 - 13:30 Lunch
13:30 - 17:00 Self study

Friday 2019-08-30
09:00 - 10:30 Discussion session
10:30 - 11:00 Coffee Break
11:00 - 12:30 work on Notebook 5
12:30 - 13:30 Lunch
13:30 - 17:00 Self study


Friday 2018-09-13 Poster session (full day): Room 105, Main Buildung, University of Bern
 
09:00 - 12:30 Poster session 1
12:30 - 13:30 Lunch
13:30 - 17:00 Poster session 2
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 practise 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.

This online book covers most of and more than Module 2. 

http://greenteapress.com/wp/think-stats-2e/
Lecturers and Coaches
Prof. Dr. Géraldine Conti 
Géraldine studied physics at EPFL, Lausanne. She worked in high energy physics in collaboration with CERN for ten years. Then, she joined the Disney Research lab in Zurich, where she specialized in Machine Learning. Today, she is working as associate Professor at the University of Applied Sciences in Yverdon-les-Bains. Her research focuses on Machine Learning. 


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.