CAS in Applied Data Science
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.
Registration for the CAS ADS 2022/23 is closed!
|Degree||Certificate of Advanced Studies in Applied Data Science ADS University of Bern (CAS ADS Unibe)|
|Length||August 2022 - July 2023|
|Flexible entry possible||Yes|
|Single module visitable||Yes|
|Place||University of Bern; Bern Winter School in Mürren, Berner Oberland; Bern Autumn School in Mallorca, Spain
Due to the pandemic situation and the related official requirements, we would like to point out that th modules are alwys accessible digital.
|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}.|
|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 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
- 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.
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 Mallorca, Spain and Module 6, which takes place in the ski resort Mürren two train hours from Bern city.
|ExWi||Exakte Wissenschaften (Sidlerstrasse 5)|
|UniM||Uni Mittelstrasse (Mittelstrasse 43)|
|HG||Main Building (Hochschulstrasse 4)|
|VR||VonRoll (Fabrikstrasse 8)|
|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||
|2022-08-24||09:00-17:00||HG 106||M1 Data acquisition and management 1||PD Dr. S. Haug||Link to Ilias|
|2022-08-25||09:00-17:00||HG 106||M1 Data acquisition and management 2||Prof. Dr. K. Brünnler|
|2022-08-26||09:00-17:00||HG 106||M1 Data acquisition and management 3||Martina Jakob, Sebastian Heinrich||Apero at 5 pm|
|2022-08-30||09:00-12:30||HG 106||M2 Statistical Inference for Data Science 1||Dr. A. Muehlemann||Link to Ilias|
|2022-08-31||09:00-12:30||HG 106||M2 Statistical Inference for Data Science 2|
|2022-09-01||09:00-12:30||HG 106||M2 Statistical Inference for Data Science 3|
|2022-09-02||09:00-17:00||HG 106||M2 Statistical Inference for Data Science 4||Apero at 5 pm|
|2022-10-XX||09:00-17:00||TBD||M2 Project Presentation Day||Project presentations. Dates to be found during module.|
08:00-12:30 17:00 -18:30
|Online / Mallorca||M3 Data Analysis and Machine Learning 1||Dr. A. Marcolongo, Dr. M. Vladymyrov et al.||
Online and Mallorca.
|Online / Mallorca||M3 Data Analysis and Machine Learning 2|
|Online / Mallorca||M3 Data Analysis and Machine Learning 3|
|2021-09-30||08:00-12:30||Online / Mallorca||M3 Data Analysis and Machine Learning 4|
|2022-10-31||Submission deadline for M1 report||Upload your report here|
|2022-11-XX||M3 Presentation day||Project presentations. Dates to be found during module.|
|2022-10-14||09:00-17:00||HG 304||M4 Ethics and Best Practices: Introduction to IT Security for Data Scientists||M. Seitz / D. Weber||Link to Ilias|
|2022-10-21||09:00-12:30||PT 323||M4 Ethics and Best Practices 1||PD. Dr. S. Haug||Link to Ilias|
|2022-10-28||09:00-12:30||PT 323||M4 Ethics and Best Practices 2 - Git 1||A. Alhineidi||Link to Ilias|
|2022-11-04||09:00-12:30||PT 323||M4 Ethics and Best Practices 3 - 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|
|2022-11-11||09:00-12:30||PT 323||M4 Ethics and Best Practices 4 - Documentation||PD Dr. S. Haug||Link to Ilias|
||PT 323||M4 Ethics and Best Practices 4 - Licences, Security||S. Marazza||Link to Ilias|
|2022-11-25||09:00-12:30||PT 323||M5 PEG 1 (4 groups a 6 participants)||PD. Dr. S. Haug||Link to Ilias|
|2022-12-02||09:00-12:30||PT 323||M5 Data science - selected readings||Link to Ilias|
|2022-12-09||09:00-12:30||PT 323||M5 Ethics - selected readings||Link to Ilias|
|2022-12-16||09:00-12:30||PT 323||M5 PEG 2 (4 groups a 6 participants)||Link to Ilias|
|2023-01-22||M5 Deadline Peer Consulting Report||Upload your report here|
|2023-01-22||M4 Deadline GitHub Repository||Upload link to repository here|
|2023-01-23||Mürren||M6 Deep Learning||Dr. G. Conti, Dr. M. Mykhailo et al.||Online and in Mürren. Link to Ilias|
|2023-01-24||08:00-12:30||Mürren||M6 Deep Learning|
|2023-01-25||08:00-12:30||Mürren||M6 Deep Learning|
|2023-01-26||08:00-12:30||Mürren||M6 Deep Learning|
|2023-01-27||08:00-12:30||Mürren||M6 Deep Learning|
|2023-03-XX||09:00-17:00||M6 Presentation day||Dr. G. Conti, Dr. M. Mykhailo||Date to be found during module.|
|2023-06-15||24:00||Project Delivery Deadline||Upload your project here|
CAS Completion Notification
|PD. Dr. S. Haug||Per email.|
|2023-08-25||17:00-21:00||TBD||BBQ for finisher|
For Module 5, two peer transfers and one oral exam are required.
Organising institution and faculty
The Certificate of Advanced Studies (CAS) in Applied Data Science (ADS) is offered by the Mathematical Institute.
- Prof. Dr. Jan Draisma (chair)
- Prof. Dr. Paolo Favaro
- PD Dr. Sigve Haug (director of studies)
- Prof. Dr. Christiane Tretter
- Prof. Dr. Thomas Wihler
- 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
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.
Application and tuition fees
Per year there are 24 places. Registrations are accepted in the order they arrive. A waiting list is maintained.
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.
"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."
University of Bern
"With this CAS, the former "black box" of machine learning turned into a very useful and powerful magic box!"
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."
"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!"
CAS Advanced Machine Learning
|Cost||CHF 9'600 inkl. Vollpension-Hotel in Mallorca|
Dieser CAS AML bietet Ihnen die Möglichkeit, Ihre Kompetenzen in Datenwissenschaft durch Kenntnisse und Fähigkeiten im Bereich maschinelles Lernen und künstliche Intelligenz zu vertiefen und zu erweitern.