CAS in Applied Data Science

Mathematical Institute

CAS 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 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.

 

Summary
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 2023/06/01
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 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.

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.

Building abbreviations
Abbreviation Building 
ExWi Exakte Wissenschaften (Sidlerstrasse 5) 
UniM Uni Mittelstrasse (Mittelstrasse 43)
PT Parkterrasse 14
HG Main Building (Hochschulstrasse 4) 
VR VonRoll (Fabrikstrasse 8)
CAS ADS Schedule 2022/23 (repeated annually)
Date Time   Room  Module  Lecturer  Comment
           
2022-02-28 and 2022-04-25 17:00-18:00 

HG 105 /
Online

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

Link to Ilias

           
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

M4 Ethics and Best Practices Excourse: Machine Learning Limitations - Shortcuts, Errors and Adversarial Attacks

Prof. Dr. Felix Wichmann Voluntary for CAS ADS participants, Link to Ilias
2022-11-18
09:00-12:30
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
2022-12-02
2022-12-09

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
2023-01-25
2023-01-26
2023-01-27

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.
2023-04-XX
2023-05-XX
09:00-17:00   Final Project Coaching   Form for selecting slots comes 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. 
           
2023-08-25 17:00-21:00 TBD Graduation Event    

 

For Module 5, two peer transfers and one oral exam are required.

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
April (TBD) TBD TBD Introduction to CAS ADS PD. Dr. S. Haug Link to Zoom Meeting will appear here
May (TBD) TBD TBD Introduction to CAS ADS PD. Dr. S. Haug Link to Zoom Meeting will appear here
June (TBD) TBD TBD 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.

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 will appear here

Mathematical Methods for Data Science and Machine Learning

2023-08-15 - 2023-08-18 (3 days) 09:15 - 17:00 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 will appear here

Modules

All Information about Modules 1-6. 

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 
(4 Days)
09:15 - 17:00 UT

F-106

PD Dr. S. Haug, Prof. Dr. K. Brünnler, Martina Jakob, Sebastian Heinrich Link to Ilias course will appear here
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 Link to Ilias course will appear here
M2 Project Presentation  TBD during the module

Module 3

M3 Data Analysis and Machine Learning 2023-09-25 - 2023-09-29 
(5 Days)
08:30 - 12:30
17:00 - 19:00
TBD (Mediterranean coast) Dr. A. Marcolongo, Dr. M. Vladymyrov et al. Link to Ilias course will appear here. 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 TBD PD Dr. S. Haug, M. Seitz, D. Yurovsky, A. Alhineidi et al.  Link to Ilias course will appear here
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 TBD PD Dr. S. Haug Link to Ilias course will appear here
M5 Peer Consulting Report deadline TBD

Module 6

M6 Deep Learning 2024-01-15 - 2024-01-19
(5 Days)
08:30 - 12:30
17:00 - 19:00
Hotel Regina Mürren 
(Bernese Oberland)
Dr. G. Schaller Conti, Dr. M. Vladymyrov et al. Link to Ilias course will appear here. 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

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!

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.-

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.

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

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