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 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.
Degree | Certificate of Advanced Studies in Applied Data Science ADS University of Bern (CAS ADS Unibe) |
---|---|
Start | 08/2023 |
Length | August 2023 - July 2024 |
Scope | 16 ECTS |
Cycle | Annual |
Flexible entry possible | Yes |
Single module visitable | Yes |
Place | University of Bern; Mürren, Bernese Oberland (Module 6); Mediterranean coast (Module 3) Due to the pandemic situation and the related official requirements, we would like to point out that the modules are always accessible online. |
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 | 2023/08/01 |
Cost | CHF 9'600 |
Special Offer | Employees and Students of the University of Bern: CHF 5'600. |
Organising institutions | Mathematical Institute |
About the program
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.
Modules
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.
Schedule 2022/23
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 Mallorca, Spain and Module 6, which takes place in the ski resort Mürren two train hours from Bern city.
Abbreviation | Building |
---|---|
ExWi | Exakte Wissenschaften (Sidlerstrasse 5) |
UniM | Uni Mittelstrasse (Mittelstrasse 43) |
PT | Parkterrasse 14 |
HG | Main Building (Hochschulstrasse 4) |
VR | VonRoll (Fabrikstrasse 8) |
Date | Time | Room | Module | Lecturer | Comment |
---|---|---|---|---|---|
2022-02-28 and 2022-04-25 | 17:00-18:00 |
HG 105 / |
Introduction to the CAS ADS | PD. Dr. S. Haug | One introduction is mandatory. No registration needed. Join online here. |
2022-08-22 | 09:00-17:00 | UniM 124 | Introduction to Python |
Voluntary |
|
2022-08-24 2022-08-25 2022-08-26 |
09:00-17:00 | HG 106 | M1 Data acquisition and management | PD Dr. S. Haug, Prof. Dr. K. Brünnler, Martina Jakob, Sebastian Heinrich | Link to Ilias. Last day with apero at 5 pm. |
2022-08-30 2022-08-31 2022-09-01 2022-09-02 |
09:00-12:30 | HG 106 | M2 Statistical Inference for Data Science | Dr. A. Muehlemann | Link to Ilias. Last day with apero at 5 pm. |
2022-10-XX | 09:00-17:00 | TBD | M2 Project Presentation Day | Project presentations. Dates to be found during module. | |
2022-09-27 2022-09-28 2022-09-29 2022-09-30 |
08:30-12:30 17:00 -19:00 |
Online / Mallorca | M3 Data Analysis and Machine Learning | Dr. A. Marcolongo, Dr. M. Vladymyrov et al. |
Link to Ilias. Module ends on Friday at 12:30. |
2022-10-31 | Submission deadline for M1 report | Upload your report here | |||
2022-10-14 | 09:00-17:00 | HG 304 | M4 Introduction to IT Security for Data Scientists | M. Seitz, D. Yurovsky | Link to Ilias |
2022-10-21 | 09:00-12:30 | PT 323 | M4 Ethics and Best Practices 2 - Best Practices and Documentation | PD. Dr. S. Haug | Link to Ilias |
2022-10-28 | 09:00-12:30 | PT 323 | M4 Ethics and Best Practices 3 - Git 1 | A. Alhineidi | Link to Ilias |
2022-11-04 | 09:00-12:30 | PT 323 | M4 Ethics and Best Practices 4 - Git 2 | A. Alhineidi | Same link as for Git 1 |
2022-11-10 | 15:15-18:00 | HG 331 | Prof. Dr. Felix Wichmann | Voluntary for CAS ADS participants, Link to Ilias | |
2022-11-18 |
|
PT 323 | M4 Ethics and Best Practices 5 - Licences, Security | S. Marazza | Link to Ilias |
2022-11-30 | Deadline for M4 GitHub repository | Link for adding link to your GitHub Repository | |||
2022-11-24 2022-12-12 |
13:30-17:00 | Zoom and HG 204 | Module 3 Project Presentations | S. Haug, M. Mykhailo, A. Marcolongo | Link to Ilias |
2022-11-25 |
09:00-12:30 | PT 323 | M5 Peer Consulting and Selected Readings | PD. Dr. S. Haug | Link to Ilias |
2022-12-31 | M5 Deadline Peer Consulting Report | Upload your report here | |||
2023-01-24 |
08:00-12:30, 17:00-19:00 | Mürren | M6 Deep Learning | Dr. G. Conti, Dr. M. Mykhailo et al. | Online and in Mürren. Link to Ilias. Module ends on Friday at 12:30 |
2023-03-XX | 09:00-17:00 | M6 Presentation day | Dr. G. Conti, Dr. M. Mykhailo | Date to be found during module. | |
2023-05-08 | 09:00-17:00 | Final Project Consultation | Voluntary. If you have questions regarding your final project, you can book a slot here. | ||
2023-06-15 | 24:00 | Final Project Delivery Deadline | Upload your project here. | ||
2023-07-31 | 24:00 |
CAS Completion Notification |
PD. Dr. S. Haug | Per email. | |
2023-09-01 | 17:00-22:00 | Münstergasse 61 Bern | Graduation Event | Lesbar. Please register here. |
For Module 5, two peer transfers and one oral exam are required.
Schedule 2023/24
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.
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 will appear here |
Introductionary courses
Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.
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
F006 |
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.
Module | Course Title | Date | Time | Location | Lecturer(s) | Comments |
---|---|---|---|---|---|---|
Module 1 |
M1 Data acquisition and management | 2023-08-23 - 2023-08-25 (3 Days) |
09:15 - 17:00 | UT
F-106 |
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 | 2023-08-29 - 2023-09-01 (4 Days) |
09:15 - 17:00 | 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. | On Friday the module ends at 12:30 |
M3 Project Presentation | TBD | |||||
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 |
M4 GitHub Repository deadline | TBD | |||||
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 coaching | TBD | TBD | TBD | ||
Final Project deadline | TBD |
Events and other important dates
Title | Date | Time | Location | Comments |
---|---|---|---|---|
CAS Apero |
2023-08-25 | TBD | TBD | Come together and have a drink or two |
CAS Completion Notification |
2024-07-31 | Be informed that you have completed the CAS programme | ||
CAS Graduation Celebration |
late August / early September | TBD | TBD | Celebrate your Graduation! |
Organising institution and faculty
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.
Admission
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
Application and tuition fees
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.-
Employees & Students 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.
Testimonials
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

Claire Dové
DI & DO

Sigve Haug
MO-FR
Petra Müller
MO-FR (E-Mail)
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