CAS Blockchain, AI and Data Science for Finance

Are you looking to change the world with artificial intelligence, blockchain and data science? Learn how to profit from the latest technologies in this CAS.

Digitalisation and innovation will define society, business and industry in the 21st century. Artificial Intelligence and machine learning will be key drivers behind the transformation to a digital world. Big data methodology and data science are used to analyse and transform data, supplying a basis on which decisions are made. Blockchain helps us to process data in a decentralised environment.

If you work at the interface between IT, business and management and are keen to discover and apply the new methodologies and techniques of digitalisation, this CAS is the perfect choice for you:

You understand the basic principles of artificial intelligence and machine learning.
You see positive advantages of digital transformation and its impact on your business.
With the aid of specific examples, you are able to apply the methodologies of Artificial Intelligence.
You will understand the process of data science, from data preparation to analysis and interpretation.
You are ready for data-driven transformation in the digital environment.
You recognise the potential that data and Artificial Intelligence hold for your organisation.

Factsheet

Degree/Certificate Certificate of Advanced Studies (CAS)
Duration 17 study days
Schedule Thursday, Friday
Application deadline 6 weeks before the course begins, or later if spaces are available
ECTS credits 12 ECTS credits

Costs CHF 8,500
Teaching language German/English
Location Schwarztorstrasse 48, Bern
School Business School
Next session Spring 2023

Contact

Downloads

Content + Structure

Portrait

Data-oriented, networked, integrated. In this part-time continuing education programme, we will familiarise you with the key concepts behind blockchain, Artificial Intelligence and Data Science. We will show you where the methodologies and techniques of machine learning can be used and outline the principles of data science, artificial intelligence and blockchain. You will explore the full spectrum of digitalisation, from theory, data evaluation and the use of new methodologies to concrete analysis and visualisation of data using case studies from the finance industry.

This CAS is designed to take you on a journey, starting with the technical principles before moving on to active implementation and finally to strategic-level approaches to these new methodologies. You will recognise the opportunities that data and Artificial Intelligence offer and exploit its potential to gain a competitive edge. Because the future belongs to data and to the new digital methodologies and models.

Learning outcomes

The CAS Blockchain, Artificial Intelligence and Data Science is designed to support you in your digital transformation to a data-driven organisation. You will become acquainted with key concepts, models and tools of applied data science for your day-to-day operations. You will become proficient in the language of data scientists and be able to represent and advance your interests in interdisciplinary projects.

The CAS will focus on strengthening the following abilities at strategic and operative level:

You will be aware of technical concepts, models and tools in the field of Data Science and be able to apply these specifically to the finance industry.
You will be aware of essential innovations and trends in Artificial Intelligence and data science and be able to identify application scenarios and configuration options for your organisation.
You will combine IT and Data Science and use data storytelling for visual communication, adding colour and life to ‘blank data’.
You will use your solid theoretical knowledge of machine learning as a basis for understanding digital projects within your organisation.
You will be able to use a programming language (R/Python) to put the theoretical models into practice on the basis of a series of specific use cases.
You will improve your decision-making and the operative and strategic value creation of your products, services, business models and strategies on the basis of data and Artificial Intelligence. In the process, you will identify fields of action that will improve your organisation on an ongoing basis.

Content

Foundations of Data Science: Data Analysis and Data Visualisation

Day 1: Introduction to Data Analysis using R
Day 2: Introduction to Data Visualisation using R – Data narratives
Topics in Finance: Business Models, Sustainable and Decentralised Finance
Day 3: Introduction to Decentralised Finance
Day 4: Introduction to Sustainable Finance
Day 5: Data-driven Business Models, Service & Product Development, Data as Innovation & Innovation as Data

Machine Learning and Artificial Intelligence in Finance

Day 6: Introduction to and applications of Machine Learning in Finance: Use cases for supervised learning; classification, regression, clustering and PCA
Day 7: Introduction to and applications of Machine Learning in Finance: Use cases for unsupervised learning; support vector machines; random forests
Day 8: Introduction to and applications of Artificial Intelligence in Finance: Neural networks
Day 9: Introduction to and applications of Artificial Intelligence in Finance: Neural networks, Trustworthiness and Explainability of Algorithms
Day 10: Practitioners’ viewpoint: Experts from the Finance Industry on recent developments

Blockchain, Distributed Ledger and Smart Contracts

Day 11: Foundations of Blockchain and Distributed Ledgers
Day 12: Bitcoin, Blockchain and Applications
Day 13: Case Study: Creating a smart contract in the Blockchain
Day 14: Regulatory topics for Blockchain

Big Data and Cloud Computing

Day 15: Introduction to Big Data and Cloud Computing

Applications and final presentations

Day 16: Applications and final presentations
Day 17: Applications and final presentations

Teaching + Methodology

Adults learn differently. That is why the continuing education at the BFH Business school is committed to modern adult education. Our mission with «brain, heart and hand» is an important principle in the design of our offers. Questions in the lessons are based on the participants' professional and practical life experiences. Contact study and self-study phases are combined in a meaningful way, analogue learning environments are combined with digital tools. Microsoft Teams is available as a supplementary tool: you have access to documents, messages and information as well as a chat function within the class.

You will benefit from effective teaching approaches that will advance your professional and personal development:

Lectures, tutorials, exchange of experience
Active use of select AI/ML techniques
Active implementation of use cases in R and Python
Presentations and technical discussions
Group work and action learning
Study of examples with reflection, case studies

Assessments

Class attendance

Regular attendance in class as well as the passing of the proof of competence are prerequisites for the successful completion of the degree programme. If you are absent for more than 2 days, you will have to make up for this (after consultation with the head of the degree programme).

Assessments in this CAS

Transfer report
Applied project
Presentation

Proof of competence transfer report

Content: The transfer report deals with a question on a topic of your CAS. You choose a topic from your own working environment and solve a practical problem.
Methodology: You develop an application-oriented solution to your practice-oriented problem. In doing so, you will use existing specialist literature, apply scientific methods such as interviews or surveys to find a solution and undertake a critical reflection.
Length: maximum 3000 words (without title page, table of contents, summary, tables, pictures, references and appendix).
Language: German (English or French by arrangement)

Additional options at no extra cost

We offer short, free learning events for all students of our CAS programmes. With these offers we would like to support you in completing your CAS successfully.

Effective learning in continuing education at universities of applied sciences

You will learn about transfer-oriented learning models and techniques. You reflect on your own learning biography and are encouraged to try out new learning behaviour.

Workshop Transfer Report

You will be supported in writing your report and benefit from active peer counselling. You will present your unfinished report and receive feedback from the audience as well as from an expert.

Methodology Transfer Report

You will get to know and understand the exact tasks of a transfer report. We teach you the quality criteria to write a good report.

We highly recommend participation for your first CAS. You will receive further information and schedule before the start of the study.

Lecturers on your CAS

Lecturers

Our lecturers are proven experts with extensive practical and theoretical knowledge in their subject areas. They have at least a university degree plus many years of experience in higher education.

Professor Jörg Osterrieder, Institute of Applied Data Science & Finance, Bern University of Applied Sciences
«Artificial intelligence is changing the ways in which we draw conclusions from data.»

Professor Branka Hadji Misheva, Institute of Applied Data Science & Finance, Bern University of Applied Sciences
«Data science is the foundation for the future of our society and businesses.»

Co-Head of CAS Programme

Admission + Further study

Do you like numbers, data and facts? Are you technology-minded? And are you looking to use these strengths to further your career? Then this CAS is the perfect choice for you. We do not require you to have any prior knowledge of statistics or IT. Join us and immerse yourself in the world of machine learning, artificial intelligence and blockchain. We will make sure you stay afloat

Target group

Typical participants:

People with an interest in the digital transformation
Project managers working in new fields such as artificial intelligence
IT staff and developers with an interest in machine learning and data science
Staff from business areas that come into contact with digital products and topics
People who are interested in new technologies and innovation
(Junior) executives in middle or senior management
Specialists from the fields of finance & controlling who are interested in advising on the design of data-based processes
Employees in process management (process owners, process managers, process analysts)

Admission requirements

University degree

You will be admitted to the programme with a degree from a university, university of applied sciences, university of teacher education, Swiss Federal Institute of Technology, or an equivalent foreign university and at least two years of professional experience.

Higher vocational training

If you have a qualification from a higher vocational education institution (Diploma Höhere Fachschule HF, Federal Diploma, Federal Certificate of Proficiency) and several years of professional experience, you will also be admitted to the programme. However, you will be required to attend our Introduction to Academic Study before starting the CAS. Completion of this module is a prerequisite for admission to the CAS.

Exceptions

In exceptional circumstances applicants may still be admitted. The head of the degree programme will make a decision based upon your application and extenuating circumstances. Please contact us. If you are admitted, you will in any case attend the course «Introduction to Academic Writing» (in German) before the first CAS.

Language skills

Individual modules are delivered in German/English, depending on the lecturer. The course materials are generally in English.

No prior knowledge of programming required

You do not need any prior experience of programming. You must be willing to get to grips with Python and R so that you can apply the techniques you will learn.

Title + Qualification

Certificate of Advanced Studies (CAS) of Bern University of Applied Sciences in «Blockchain, AI and Data Science for Finance» (12 ECTS credits).

Combination with further study programmes

This CAS can be counted towards:

Organisation + Registration

The course comprises 17 full days of classroom teaching, always on Thursdays and Fridays, over a period of seven months. Three months are allotted for the final paper. Max. 24 participants.

Register online

Spring 2023 registration

Documents required for registration

You can register online via registration form. For registration we require following documents (in PDF format, max. 1 MB per document):

Diplomas
Curriculum Vitae
Passport-sized photo (in JPG format)

Please upload these documents even if you have already submitted them for another application.

Organisational aspects of the CAS

Registration deadline + Timetable

Registration closes six weeks before the course begins. The registration deadline will be extended if spaces are still available.

The timetable will be published here around six months before the start of the course.

Costs

CHF 8'500

All compulsory literature, course materials and certificates of competence are included in the price.

Terms and conditions

The continuing education regulations of the Bern University of Applied Sciences and the implementation regulations for the continuing education regulations apply. The right to make changes is reserved. In case of doubt, the wording of the legal provisions and regulations shall prevail.

Continuing Education Regulations of Bern University of Applied Sciences (WBR) (PDF, 133 KB)

Implementation Provisions for the Continuing Education Regulations WBR in the Business School (PDF, 81 KB)

Implementation Provisions for the Continuing Education Regulations (PDF, 77 KB)

Framework Study Regulations at Bern University of Applied Sciences (PDF, 127 KB)

Regulations on Scientific Integrity at Bern University of Applied Sciences (WissIR) (PDF, 140 KB)

Legal foundations

Advice + Information events

In our FAQ you will find answers to many questions about the administration and organisation of our continuing education programme (available in German).