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 6 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 hybrid mode, i.e. remote participation is always possible. There are 20 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.

CAS Applied Data Science
Summary
Degree Certificate of Advanced Studies in Applied Data Science ADS University of Bern (CAS ADS Unibe)
Start 08/2024
Length August 2024 - July 2025
Scope 16 ECTS
Cycle Annual
Flexible entry possible Yes
Single module visitable Yes
Place University of Bern; Mürren, Bernese Oberland (Module 6); Giens peninsula, southern France (Module 3)
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).
Registration until 2024/07/15
Cost CHF 9'600
Special Offer Students and Employees 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 Certificate 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 block 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 block 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 3 traditionally takes place at the mediterranean coast. Full pension hotel accommodation is included in the CAS fee. 

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: Peer Consulting and Selected Readings

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.

Module 6 traditionally takes place in the wonderful historic hotel Regina in the ski resort Mürren (Bernese oberland), only about two train hours from Bern. Full pension hotel accommodations are included in the CAS fee. 

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.

Building Abbreviations
Abbreviation Building
ExWi Exakte Wissenschaften (Sidlerstrasse 5)
UniM Uni Mittelstrasse (Mittelstrasse 43)
HG Main Building (Hochschulstrasse 4)
PT Parkterrasse 14
UT Unitobler (Muesmattstrasse)

Information Events

Learn everything you need to know about the CAS in Applied Data Science. One introduction is mandatory, remote participation is possible. 

Introduction Events 2023
Date Time Location Title Lecturer Comments
2023-03-27 18:15 - 20:00 HG 212 Introduction to CAS ADS PD. Dr. S. Haug Link to Zoom Meeting.
2023-06-19 18:15 - 20:00 HG 212 Introduction to CAS ADS PD. Dr. S. Haug Link to Zoom Meeting. Slides.

Introductionary courses

Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.

Data Science Fundamentals 2023
Title Date Time Location Lecturer Description Comments

Introduction to Programming (Python)

2023-08-14 09:15 - 17:00 UT

F-123

Dr. K. Sipos
Attendance is recommended for CAS ADS students who wish to refresh their Python programming knowledge or who are new to the Python programming language
Link to Ilias course

Mathematical Methods for Data Science and Machine Learning

2023-08-15 - 2023-08-18 (4 half days) 09:15 - 12:30 UT

F-121

Dr. K. Sipos This course is intended for CAS AML students, but interested CAS ADS participants who wish to deepen their mathematical knowledge and learn about machine learning mathematics are welcome too.  Link to Ilias course

Modules

All Information about Modules 1-6. 

Course materials are accessed via the Ilias learning platform.

CAS ADS Modules 2023/2024
Module Course Title Date Time Location Lecturer(s) Comments

Module 1 

M1 Data acquisition and management  2023-08-23 - 2023-08-25 
(3 Days)
09:00 - 17:00 UT

F-106

PD Dr. S. Haug, Prof. Dr. K. Brünnler, Martina Jakob, Sebastian Heinrich
M1 Project Report deadline 2023-10-31

Module 2

M2 Statistical Inference for Data Science 2023-08-29 - 2023-09-01 
(4 half days, afternoons for self-study)
09:00 - 12:30 UniM

124

Dr. A. Mühlemann
M2 Project Presentation  TBD during the module

Module 3

M3 Data Analysis and Machine Learning 2023-09-25 - 2023-09-29 
(4 Days)
08:30 - 12:30
17:00 - 19:00
Hotel Combo Venice Dr. A. Marcolongo, Dr. M. Vladymyrov et al. Monday is arrival day - On Monday, the module content starts 17:00. On Friday the module ends at 12:30
M3 Project Presentation 2023-11-27 and 2023-12-1

Module 4

M4 Ethics and Best Practices Every Friday
2023-10-20 until 2023-11-17
09:15 - 12:30 ExWi 
B77
PD Dr. S. Haug, M. Seitz, D. Yurovsky, A. Alhineidi et al.  On Friday, 2023-10-27, the course is from 09:15 -17:00 in Room A 027 in UniS
M4 GitHub Repository deadline 2023-11-30

Module 5

M5 Peer Consulting and selected readings Every Friday
2023-11-24 until 2023-12-15
09:15 - 12:30 ExWi 
B77
PD Dr. S. Haug
M5 Peer Consulting Report deadline TBD

Module 6

M6 Deep Learning 2024-01-15 - 2024-01-19
(4 Days)
08:30 - 12:30
17:00 - 19:00
Hotel Regina Mürren 
(Bernese Oberland)
Dr. G. Schaller Conti, Dr. M. Vladymyrov et al.  On Friday the module ends at 12:30
M6 Project Presentation TBD

Final Project

Final Project Feedback Session
Submission Deadline
May 15

June 15 
13:30 - 16:00 Main Building 331 About the final project and the graduation event here.
Graduation event August 30 More information under link above (Final Project)
Building Abbreviations
Abbreviation Building
ExWi Exakte Wissenschaften (Sidlerstrasse 5)
UniM Uni Mittelstrasse (Mittelstrasse 43)
HG Main Building (Hochschulstrasse 4)
PT Parkterrasse 14
UT Unitobler (Muesmattstrasse)

Information Events

Learn everything you need to know about the CAS in Applied Data Science. One introduction is mandatory, remote participation is possible. 

Introduction Events 2024
Date Time Location Title Lecturer Comments
2024-03-11 18:15 - 20:00 HG 208 Introduction to CAS ADS PD. Dr. S. Haug Link to Zoom Meeting 
2024-06-12 18:15 - 20:00 HG 208 Introduction to CAS ADS PD. Dr. S. Haug Link to Zoom Meeting 

Introductionary courses

Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.

Data Science Fundamentals 2024
Title Date Time Location Lecturer Description Comments

Introduction to Programming (Python)

2024-08-12 09:15 - 17:00 UT, room F -121 Dr. K. Sipos
Attendance is recommended for CAS ADS students who wish to refresh their Python programming knowledge or who are new to the Python programming language
Link to Ilias course will appear here

Mathematical Methods for Data Science and Machine Learning

2024-08-13 - 2024-08-16 (4 half days) 09:15 - 12:30 UT, room F -121 Dr. K. Sipos This course is intended for CAS AML students, but interested CAS ADS participants who wish to deepen their mathematical knowledge and learn about machine learning mathematics are welcome too.  Link to Ilias course will appear here

Modules

All Information about Modules 1-6. 

Course materials are accessed via the Ilias Learning Platform. The Link to the course will appear here. 

CAS ADS Modules 2024/2025
Module Course Title Date Time Location Lecturer(s) Comments

Module 1 

M1 Data acquisition and management  2024-08-21 - 2024-08-23 
(3 Days)
09:00 - 17:00 Wednesday, 2024-08-21: UniM, room 120

Thursday, 2024-08-22: UniM, room 220

Friday, 2024-08-23: UniM, room 220

PD Dr. S. Haug, Prof. Dr. K. Brünnler, Martina Jakob, Sebastian Heinrich
M1 Project Report deadline TBD

Module 2

M2 Statistical Inference for Data Science 2024-08-27 - 2024-08-30
(4 half days, afternoons for self-study)
09:00 - 12:30 Tuesday, 2024-08-27: UniM, room 120

Wednesday, 2024-08-28: UniM, room 320

Thursday, 2024-08-29: UniM, room 324

Friday, 2024-08-30: UniM, room 224

Dr. A. Mühlemann
M2 Project Presentation  TBD 

Module 3

M3 Data Analysis and Machine Learning 2024-09-23 - 2024-09-27 
(4 Days)
08:30 - 12:30
17:00 - 19:00
Giens Peninsula near Hyères (southern France) Dr. A. Marcolongo, Dr. M. Vladymyrov et al. Monday is arrival day - On Monday, the module content starts 17:00. On Friday the module ends at 12:30
M3 Project Presentation TBD

Module 4

M4 Ethics and Best Practices Every Friday
2024-10-18 until 2024-11-15
13:15 - 17:00 ExWi, room B077
PD Dr. S. Haug, M. Seitz, D. Yurovsky, A. Alhineidi et al. 
M4 GitHub Repository deadline TBD

Module 5

M5 Peer Consulting and selected readings Every Friday
2024-11-22 until 2024-12-13
13:15 - 17:00 ExWi, room B077 PD Dr. S. Haug
M5 Peer Consulting Report deadline TBD

Module 6

M6 Deep Learning 2025-01-13 - 2025-01-17
(4 Days)
08:30 - 12:30
17:00 - 19:00
Hotel Regina Mürren 
(Bernese Oberland)
Dr. G. Schaller Conti, Dr. M. Vladymyrov et al.  Monday is arrival day - On Friday the module ends at 12:30
M6 Project Presentation TBD

Final Project

Final Project coaching TBD TBD TBD
Final Project deadline TBD

Events and other important dates

Informal events, other dates
Title Date Time Location Comments

CAS Apero

2024-08-23 17:00 TBD Come together and have a drink or two

CAS Completion Notification

2025-07-31 Be informed that you have completed the CAS programme

CAS Graduation Celebration

late August / early September 2025 TBD TBD Celebrate your Graduation!

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

Lecturers            

  • Prof. Dr. Dr. Claus Beisbart 
  • Prof. Dr. Kai Brunnler 
  • Dr. Geraldine Schaller Conti 
  • PD Dr. Sigve Haug 
  • Dr. Kinga Sipos 
  • Dr. Mykhailo Vladymyrov 
  • Dr. Guillaume Witz 
  • et al. 

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 APPLICATION ORIENTED PROFESSIONALS ► Make your own applications with your own data

RELEVANT FOR DATA ANALYSTS ► Go beyond spread sheets towards large data sets and refine their skills

APPLICABLE TO CONSULTANTS ► Know the possibilities offered by data science

INTENDED FOR RESEARCHERS ► Take data science expert roles within your 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 information events. Attendance to one information event is mandatory. Participants 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 20 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: 2023-06-01

Per year there are 20 places. Registrations are accepted in the order they arrive. A waiting list is maintained. 

Program fees

Regular CAS program: CHF 9'600.-

Students currently enrolled in an University or University of Applied Sciences: CHF 5'600.-

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

Payment in installments is possible.

lnclusive of all modules, performance assessments, certificates, materials & teaching platforms, coffee breaks, full pension hotels (Module 3 and 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

 

Anonymous

"The CAS has taken my analysis skills to a whole new level. Sigve and his team teach the students not just the methods of Data Science but the spirit of Data Science. I am very motivated to continue on this path."

Stefano Fabbri

University of Bern

"With this CAS, the former "black box" of machine learning turned into a very useful and powerful magic box!"

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

Cheyenne Friedrich

DI & MI

cheyenne.friedrich@unibe.ch

Assistant Education and Communication Manager Continuing Education in Extended Intelligence

Previous

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Degree CAS
Start 08/2024
Language Englisch
Cost CHF 9'600

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Mathematical Institute