Information Processing and Data Analytics 2025
How to Apply?
First of all, please register yourself in our DEE system to be able to access our “Event Planner” service. You will find instructions on how to create an account in the Attached Guide.
As soon as you have your account and you are logged in, you be able to find the event page of our Information Processing and Data Analytics Block Week 2025 under this link.
Please if you are interested in joining our event register yourself as soon as possible by clicking on DEE system.
NOTE: Amount of seats is limited
Important additional information about/for BIP registration
Name: Dortmund University of Applied Sciences and Arts
Faculty/Department: Computer Science
Erasmus code: D DORTMUN02
Administrative contact person: Ekaterina Hermann (ekaterina.hermann@fh-dortmund.de); position: international projects coordinator
Responsible person for signing the learning agreement: Thorsten Ruben (thorsten.ruben@fh-dortmund.de); position: international master studies coordinator
BIP title: Information Processing and Data Analytics 2025
BIP ID: tba.
Time frame: The BIP Information Processing and Data Analytics 2025, organised by the University of Applied Sciences Dortmundspans over seven consecutive calendar weeks (CWs). It starts with a virtual phase (CW 45), followed by a presence phase of one week (block week CW 49 from 1st to 5th of December) and a virtual phase (from CW 50 to CW 51).
Content of the virtual component:
Before:
- Basic introduction
- Team building
- Self learning phase, theory basics
- Introduction case study
- Preparation of physical component
- online lecture: Prof. Dr. Katja Klingebiel, Digital Supply Chain Management, 28.10.2025, 10:15-11:45
(Physical component: Blockweek => Case Study)
After:
- Finalizing groupwork
- Theory vs. Case Study
- Reflection
Course number for Learning Agreement: 94308
Course title for Learning Agreement: Information Processing and Data Analytics
ECTS: 6
Introduction
This Erasmus+ Blended Intensive Programme (BIP) responds to the growing need for interdisciplinary, practice-oriented training in data-driven decision-making across different sectors. By combining virtual and physical learning formats, it offers flexibility and accessibility for students from diverse academic and cultural backgrounds. The programme directly supports the Faculty of Computer Science’s internationalization strategy by fostering cooperation with strong European partners and promoting international, team-based project work. Its added value lies in the combination of academic input, real-world case studies, and transversal skills training (e.g. teamwork, intercultural communication, digital literacy). The BIP encourages inclusive participation and contributes to the EC’s goal of broadening access to short-term mobilities. The long-standing collaboration among the partner institutions ensures a high-quality and sustainable partnership
Learning Content and Methods
Modern project management is based on facts and on data. Dealing with data, analysing data and deriving conclusions and decisions from data is crucial for project management. The module is developing the topics of information processing and data analytics along a case study.
Information processing and data collection
- Development of indicator systems
- Design of data collection experiments with online tools
- IT tools for data collection
- Advanced MS Excel
Data bases and data warehouses
- 1 Introduction to databases, SQL
- 2 Data warehouse systems
- 3 Cloud based systems
- 4 Analysis of Case Studies
Data analytics
- Data refinement
- Data analytics and business intelligence
- Probabilistic methods
- Artificial intelligence and learning (introduction to IBM Watson)
Teaching in this module will utilize diverse methods:
Students will be introduced to the relevant topics and to literature for further reading. Students will be guided through a case study project where they set up a small experiments for data collection, data storage and query and data processing for an example case. They form teams and set up IT tools.
- Lectures introducing concepts, methods and tools
- Group work in the case study project to practice concepts and methods, to develop skills and to work on case studies
- Presentations to communicate results and do a scientific discussion and reflection
Learning Outcomes
Knowledge and Understanding:
The students
- explain the basic characteristics of data and data collection
- explain advanced functionality of Excel
- explain database and data warehouse concepts
- explain the core concepts of data analytics and business intelligence
Application and Generation of Knowledge:
The students are able to
- develop data collection experiments with online tools
- apply MS Excel for data analytics
- set up and use simple SQL databases
- set up and use tools for statistical data analysis
- use IBM Watson for AI experiments
Communication and Cooperation:
The students
- train to reflect on the impact of their work and their projects
- train to do surveys with people from different cultural backgrounds
- are able to lead discussions and bring conflicting ideas and goals to a consensus
- develop a critical attitude to data based decision making
Scientific Self-Understanding / Professionalism:
The students are able to
- develop a critical attitude to issues like privacy and data protection
- apply their judgement on controversial topics and learn to lead a team to a consensus
Organizational Framework
The BIP spans over seven consecutive calendar weeks (CWs). It starts with a virtual phase, followed by a presence phase of one week (block week) and a virtual phase as shown in the table below:
CW | Mon – Fri | Phase | Content |
CW 44 | 28-Oct | Virtual Phase | Online lecture, Prof. Dr. Katja Klingebiel, Digital Supply Chain Management 10:15-11:45 |
CW 45 | 03-Nov – 09-Nov | Virtual Phase | Introduction/Team building/Self learning phase |
CW 46 | 10-Nov – 16-Nov | Virtual Phase | Introduction/Team building/Self learning phase |
CW 47 | 17-Nov – 23-Nov | Virtual Phase | Introduction/Team building/Self learning phase |
CW 48 | 24-Nov – 30-Nov | Virtual Phase | Introduction/Team building/Self learning phase |
CW 49 | 01-Dec – 05-Dec | Presence Phase/Block Week | Lecture |
CW 50 | 08-Dec – 14-Dec | Virtual Phase | Finalizing groupwork |
CW 51 | 15-Dec – 21-Dec | Virtual Phase | Finalizing groupwork |
Student Competence Requirements
The target group are bachelor students in their last year as well as master students.
Social Events
Tba
Location
By plane
From Dortmund Airport (DTM):
Take the “Airport Shuttle Bus” towards the direction of Holzwickede station. From there, take the train in the direction “Dortmund HB”, then take the “S-Bahn S1” towards the direction of “Bochum”. Exit at “Universität”.Follow the map on the right to get to the EFS 44.
From Düsseldorf Airport (DUS):
Take the “S-Bahn S1/S21” towards the direction of Dortmund. Exit at “Universität”.Follow the map on the right to get to the EFS 44
By train (S-Bahn)
By train (S-Bahn):
From Dortmund main station: Take the “S-Bahn S1” towards the direction of “Bochum”. Exit at “Universität”. Follow the map on the right to get to the EFS44.
By subway (U-Bahn) and bus:
Take the metro U42 from the stop “Reinoldikirche”, direction Hombruch. Exit at the stop “An der Palmweide”. You can take several buses from there to campus (445, direction Otto-Hahn-Straße, 462, direction Huckarde Bushof) and 447, direction Huckarde Bushof. Exit at the stop ‘Emil Figge Strasse’ or ‘Dortmund Universität’.
Please, remember that when you go by bus in Dortmund in order to get off, you have to push the red button BEFORE your stop. Otherwise, the bus may not stop at all at the stop you need. All stops are announced.
By car
The campus is closely located to the autobahn intersection “Dortmund West”, where the autobahn A45 crosses the autobahn B1/A40. Take the exit “Dortmund Dorstfeld” on B1/A40 from where road signs lead you to the Technical University (TU Dortmund), FH Dortmund is located in the same area.
Info Guide
In the Info Guide you can find information about:
- accommodation possibilities;
- public transportation;
- places to eat and entertain;
- other additional hints.
Contact us
Event Coordinator:
Ekaterina Hermann
ekaterina.hermann@fh-dortmund.de
Content-related questions:
Prof. Dr. Christian Reimann
christian.reimann@fh-dortmund.de