2022-08-30 - 2022-09-02 CAS ADS M2 Statistical Inference for Data Science (4 Days)
CAS Applied Data Science Module 2
Tabs
About
CAS Applied Data Science Module 2
See study plan on cas-applied-datascience.unibe.ch.
Target group
See study plan on cas-applied-datascience.unibe.ch.
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 poster presentation
Practical information (time, location ...)
Time : 2022-08-30 - 09-02 09:00 - 12:30 (afternoons for self studies)
Onsite Location : University of Bern, Main Building, room 106.
Online: Link
Presentation days: 2022-10-xx (each participant only needs 0.5 day atttendance).
Language: English
Participants : Max 24
Registration : Mandatory (via Ilias)
Lecturer: Dr. Anja Mühlemann
Responsible : PD Dr. Sigve Haug
Onsite Location : University of Bern, Main Building, room 106.
Online: Link
Presentation days: 2022-10-xx (each participant only needs 0.5 day atttendance).
Language: English
Participants : Max 24
Registration : Mandatory (via Ilias)
Lecturer: Dr. Anja Mühlemann
Responsible : PD Dr. Sigve Haug
Schedule
Module 2 Statistical Inference for Data Science
Tuesday
09:00 - 10:30 Notebook 1 on descriptive statistics
10:30 - 11:00 Coffee Break
11:00 - 12:30 Introduction and discussions
12:30 - 13:30 Lunch
13:30 - 17:00 Notebook 2 on probability density functions ... (self study)
Wednesday
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
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
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 - 16:00 Self study
17:00 - 18:30 Apero
2022-10-xx Presentation day
09:00 - 12:30 Presentations
Tuesday
09:00 - 10:30 Notebook 1 on descriptive statistics
10:30 - 11:00 Coffee Break
11:00 - 12:30 Introduction and discussions
12:30 - 13:30 Lunch
13:30 - 17:00 Notebook 2 on probability density functions ... (self study)
Wednesday
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
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
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 - 16:00 Self study
17:00 - 18:30 Apero
2022-10-xx Presentation day
09:00 - 12:30 Presentations
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.kaggle.com/datasets
Not all datasets are equally well suited for all things to learn and practise in the CAS.
- 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.kaggle.com/datasets
Not all datasets are equally well suited for all things to learn and practise in the CAS.
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/
This online book covers most of and more than Module 2.
http://greenteapress.com/wp/think-stats-2e/
Lecturers and Coaches
Dr. Anja Mühlmann
Anja holds a phd in statistics from the University of Bern, loves data science and coaching.
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.
Anja holds a phd in statistics from the University of Bern, loves data science and coaching.
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.