CAS in Applied Data Science

Mathematical Institute

With the explosion of data in science, economics, administration, medicine and many other fields, the importance and the demand for data science skills are increasing. It is the scientific methods and processes of extracting knowledge and insights from data. In light of this, the University of Bern offers a Certificate of Advanced Studies (CAS) program in Applied Data Science.

It is structured in six modules, graduation is possible within one or two years. The CAS provides 16 ECTS credit points achieved in 21 days of presence with a total workload about 480 hours.. There is a strong focus on working together, however, we run all sessions in dual model, i.e. remote participation is always possible. There are 24 places each year. Our teaching methods are modern and peer oriented. The level assumes own experience and a higher education degree with some mathematical background. The program is applied in the sense of focusing on concepts and usage of common data science infrastructures and software tools, not on theoretical elaboration of the mathematics, statistics and informatics.

All places for the CAS Applied Data Science 2021/22 have been filled.

Registration for the CAS ADS 2022/23 will open in November 2021.

CAS Applied Data Science
Summary
Degree Certificate of Advanced Studies in Applied Data Science ADS University of Bern (CAS ADS Unibe)
Start 08/2021
Length August 2021 - July 2022
Scope 16 ECTS
Cycle Annual
Flexible entry possible Yes
Single module visitable Yes
Place Universität Bern, Bern Winter School in Mürren, Berner Oberland
Due to the pandemic situation and the related official requirements, we would like to point out that individual course modules or parts of them can be replaced by distance learning or postponed.
Language English
Admission Aimed at students and professionals from the public/private sector that hold a degree from a university or a university of applied sciences (e.g. BSc, MSc, PhD}.
Cost CHF 9'600
Special Offer Employees and Students of the University of Bern: CHF 5'600.
Organising institutions Mathematical Institute
Registration

With the explosion of data in science, economics, administration, medicine and many other fi elds, the importance and the demand for data science skills are increasing. Data science is a discipline consisting of applied mathematics, statistics, computer science, ethics and subject specifi c knowledge in application areas. It is the scientifi c methods and processes of extracting knowledge and insights from data. In light of this, the University of Bern off ers a Certifi cate of Advanced Studies (CAS) program in Applied Data Science. The program is organised into six modules, running over 21 course days from August to January and targets professionals and researchers in the private and public sector. The content covers a full cycle from data acquisition planning, description and visualisation of data, inference, machine learning, best practices ethics and deep learning. Our teaching methods are modern and peer oriented. The level assumes own experience and a higher education degree with some mathematical background. The program is applied in the sense of focusing on concepts and usage of common data science infrastructures and software tools, not on theoretical elaboration of the mathematics, statistics and informatics

Objectives

  • Course competence is developed throughout six modules. On completion the graduates will:
  • be familiar with different data sources, data types, and be able to develop data management plans;
  • be able to describe, extract and present scientific knowledge from data by application of statistical methods;
  • be able to process data with machine learning tools and methods;
  • be familiar with best practices for data management, analytics and science;
  • be able to analyse and communicate data science challenges and use a wide range of data science tools and methods;
  • be able to perform deep learning for a wide range of tasks.

If there are free places, modules can be attended individually.

Module 1: Data Acquisition and management

In this module, you will learn to understand different data sources and types and how to design data management models and plans.

Module 2: Statistical inference for data science

In this module, you will become familiar with typical statistical concepts for describing and analysing data. You will learn the importance of statistical inference for data science and where to apply it, along with the understanding and application of the theoretical concepts. You will learn how to draw scientific conclusions from statistical analysis results.

Module 3: Data analysis and machine learning

In this module, you will learn about standard analysis techniques and how to apply state-of-the-art machine learning with Python.

Module 4: Ethics and best practices

In this module, we reflect upon and apply best practices for data and code management, resource usage, quality assurance, open science, open access and fair principles. You will learn about and be able to discuss the ethical questions in scientific computing, and learn to use Version Control Software with Git.

Module 5: Consolidations and electives 

This module comprises This module comprises peer knowledge exchange groups, peer consultations and selected readings.

Module 6: Deep Learning

In this module, you will learn performing deep learning with TensorFlow.

All modules

The duration of all modules corresponds to approximately 20 classroom hours each and module work (expected eff ort is 30 hours), with each complete module qualifying for 2 ECTS points. The expected workload for the CAS Project is 120 hours. Main tool and language is Python.

Rooms and lecturers to be confirmed. All courses take place in walking distance from the Bern railway station. The exception is Module 3 which happens in the ski resort Mürren two train hours from Bern city.

Building abbreviations
Abbreviation Building 
ExWi Exakte Wissenschaften (Sidlerstrasse 5) 
UniM Uni Mittelstrasse (Mittelstrasse 43)
HG Main Building (Hochschulstrasse 4) 
VR VonRoll (Fabrikstrasse 8)
CAS ADS Schedule 2021/22 (repeated annually)
Date Time   Room  Module  Lecturer  Comment
           
2021-02-15 17:00-18:00  ExWi 228 Introduction to the CAS ADS  PD. Dr. S. Haug  One introduction is mandatory. Join online here.
2021-04-26 17:00-18:00 ExWi 228  Introduction to the CAS ADS PD. Dr. S. Haug No registration needed. Slides. Join online here.
2021-05-26 17:00-18:00   Introduction to the CAS ADS PD. Dr. S. Haug No registration needed. Sides. Join online here.
           
2021-08-16 09:00-17:00 UniM 124 Introduction to Python  

Voluntary

Link to Ilias course

           
2021-08-25 09:00-17:00 VR B306 M1 Data acquisition and management 1 PD Dr. S. Haug Link to Ilias course
2021-08-26 09:00-17:00 VR B306 M1 Data acquisition and management 2    
2021-08-27 09:00-17:00 VR B306 M1 Data acquisition and management 3   Apero at 5 pm. Please inidcate you attendance here.
           
2021-08-31 09:00-12:30 HG 033 M2 Statistical Inference for Data Science 1 PD Dr. S. Haug Link to course.
2021-09-01 09:00-12:30 HG 033 M2 Statistical Inference for Data Science 2    
2021-09-02 09:00-12:30 HG 033 M2 Statistical Inference for Data Science 3    
2021-09-03 09:00-12:30 HG 033 M2 Statistical Inference for Data Science 4    
2021-10-27 09:00-17:00   M2 Project Presentation Day   Project work presentations. M1 Project report submission deadline.
           
2021-09-28 08:00-12:30   M3 Data Analysis and Machine Learning 1  

Online and Mallorca. Indicate your choice here.

Link to course

2021-09-29 08:00-12:30   M3 Data Analysis and Machine Learning 2    
2021-09-30 08:00-12:30   M3 Data Analysis and Machine Learning 3    
2021-10-01 08:00-12:30   M3 Data Analysis and Machine Learning 4    
           
2021-11-XX     M2/M3 Presentation day   Project work presentations from M2 and M3. M1 Project report submission deadline. Date to be found during Module 1. 
           
2021-10-15 09:00-17:00 Room 324/325 Parkterrasse 14 M4 Ethics and Best Practices: Introduction to IT Security for Data Scientists M. Seitz / D. Weber Link to course here.
2021-10-22 09:00-12:30 Room 324/325 Parkterrasse 14 M4 Ethics and Best Practices 1 PD. Dr. S. Haug Link to course here.
2021-10-29 09:00-12:30 Room 324/325 Parkterrasse 14 M4 Ethics and Best Practices 2 - Git 1 P. Verges Link to course here.
2021-11-05 09:00-12:30 Room 324/325 Parkterrasse 14 M4 Ethics and Best Practices 3 - Git 2 P. Verges  
2021-11-12 09:00-12:30 Room 324/325 Parkterrasse 14 M4 Ethics and Best Practices 4 - Documentation PD. Dr. S. Haug Link to course here.
2021-11-19
09:00-12:30
Room 324/325 Parkterrasse 14 M4 Ethics and Best Practices 4 - Licences, Security S. Marazza Link to course here.
           
2021-11-26 09:00-12:30 Room 324/325 Parkterrasse 14 M5 PEG 1 (4 groups a 6 participants)   Link to course here.
2021-12-03 09:00-12:30 Room 324/325 Parkterrasse 14 M5 Data science - selected readings   Link to course here.
2021-12-10 09:00-12:30 Room 320, UniM M5 Ethics - selected readings   Link to course here.
2021-12-17 09:00-12:30 Room 324/325 Parkterrasse 14 M5 PEG 2 (4 groups a 6 participants)   Link to course here.
2022-01-23     M5 Deadline Peer Consulting Report   Link to course and upload here.
2022-01-23     M4 Deadline GitHub and Documentation   Link to course and upload here.
           
           
           
2022-01-24   Mürren M6 Deep Learning Dr. G. Conti, Dr. M. Mykhailo, PD Dr. S. Haug et al.   Online and in Mürren. Indicate your attendance here. Course link here.
2022-01-25 08:00-12:30 Mürren M6 Deep Learning    
2022-01-26 08:00-12:30 Mürren M6 Deep Learning    
2022-01-27 08:00-12:30 Mürren M6 Deep Learning    
2022-01-28 08:00-12:30 Mürren M6 Deep Learning    
2022-03-XX 09:00-17:00   M6 Presentation day PD. Dr. S. Haug, Dr. M. Mykhailo  
2022-03-XX 09:00-17:00   M6 Presentation day PD. Dr. S. Haug, Dr. M. Mykhailo  
           
2022-04-31 24:00   Project Delivery Deadline   Link to instructions and upload here.
           
2022-06-24 24:00  

CAS Completion Notification

PD. Dr. S. Haug Per email. 
           
2022-07-01 17:00-21:00 TBD BBQ for finisher    

 

For Module 5, 10 electives, peer knowledge transfer groups (two half days) and peer consulting (one day) are also needed. Electives are continually published under Trainings and Workshops. Two peer transfers and one oral exam are also needed for Module 5.

The Certificate of Advanced Studies (CAS) in Applied Data Science (ADS) is offered by the Mathematical Institute.

Program management

  • Prof. Dr. Jan Draisma
  • Prof. Dr. Paolo Favaro
  • PD Dr. Sigve Haug (director of studies)
  • Prof. Dr. Christiane Tretter
  • Prof. Dr. Thomas Wihler (chair)

Lecturers            

  • Prof. Dr. Dr. Claus Beisbart – University of Bern
  • Prof. Dr. Kai Brunnler – Berner Fachhochschule
  • Dr. Geraldine Conti – PAG
  • PD Dr. Sigve Haug – University of Bern
  • Dr. Qiyang Hu – University of Bern
  • Dr. Kinga Sipos – University of Bern
  • M.Sc. Pablo Verges – DECTRIS Ltd.
  • Dr. Mykhailo Vladymyrov – University of Bern
  • Dr. Guillaume Witz – University of Bern

Target groups

Aimed at students and professionals from the public/private sector that hold a degree from a university or a university of applied sciences (e.g. BSc, MSc, PhD}.

SUITABLE FOR MANAGEMENT ► wanting to know what data scientists are accomplishing in their fields

RELEVANT FOR DATA ANALYSTS ► who want to go beyond spread sheets towards large data sets and refine their skills

APPLICABLE TO CONSULTANTS ► with a desire to know the possibilities offered by data science

INTENDED FOR RESEARCHERS ► wanting to take data science expert roles within their teams

Standard data sets are provided, but participants are encouraged to bring or acquire their own. lf you have any questions regarding whether this program could work for you, please do not hesitate to contact us.

Registered participants will receive acceptance confirmation by email and will be invited to one of the next About the CAS Applied Data Science events. Attendance to one event is mandatory. Partcipants can cancel their registrations before the deadline without any costs. After the deadline the regulations apply. Individual modules and electives can be attended before the registration. Please contact PD Dr. Sigve Haug for further information.

Registration opens in November and a maximum of 24 registrations can be accepted each year. Registrations are processed in the order of arrival. The CAS can only be offered if there are sufficient registrations by the deadline.

Deadline: end of May.

Per year there are 24 places. Registrations are accepted in the order they arrive. A waiting list is maintained. 

Program fees

Regular CAS program: CHF 9'600.-

Employees & Students of University of Bern: CHF 5'600.-

Payment in instalments is possible.

lnclusive of all modules, performance assessments, certificates, materials & teaching platforms, coffee breaks, half pension hotel in Mürren (Module 6) and diploma apero.

Participants must supply their own laptops.

lf there are free places, modules can be attended individually. Prices are CHF 300.- per half day. Individual modules are accredited with certificates which are accumulated for the full CAS ADS.

Register here

 

Fluri Wieland

lnsitute of Anatomy, University of Bern

"With the CAS Applied Data Science I had a distict advantage in applying for doctoral positions."

Casimir von Arx

Mathematician, Federal Department of Foreign Affairs

"Thanks to the CAS Applied Data Science I extended my methodical know­ledge in data handling and analysis - especially in Machine Learning."

Anonymous

"Thanks to this CAS, I really got involved with Data Science. l received some great tools that helped to solve a lot of problems - and l'm hungry for more!"

Contact

Vorherige