Menu Search Nederlands

Data Science

Exchange programme

With the digitalization of societies worldwide data has become an important source of information. Both in companies and the government, the storage of data has dramatically increased over the past few decades.

The increase of the amounts of data also opens the door to new applications. More and more applications, for which these data serve as a basis, are being developed. The collection, storage, and required accessibility of the growing amount of data pose various challenges in the areas of data management and architecture. Other challenges lie in the design and implementation of new, innovative applications. The identification of such opportunities for innovation requires that an affinity with the "business" needs to be developed.


In order to take up the current challenges within the data domain, it is essential that the possible solutions are mapped out systematically with their pros and cons. Apart from literature studies, prototypes must be built to help formulate the pros and cons of each approach. In other words, innovation and research are of crucial importance in this exchange programme.


A number of the challenges mentioned have already been taken up within the scope of Big Data. Innovative applications that combine data from different databases are also being developed. The activities have in earlier years of this exchange programme resulted in a range of publications.

Facts and figures

The programme at a glance

Programme information

A view of the study programme

About the programme

The Data Science exchange programme will help you acquire knowledge and insight into how to deal with the growing amount of data. You will also learn how to use these data in the development of new applications that will give organisations a head start over their competitors.

Below are a number of areas that are covered in this exchange programme and that are necessary for the implementation of contemporary innovative applications.

  • Core concepts that pertain to the collection of data, such as privacy, transparency, accountability, identity and identification, freedom of choice, and efficiency and effectiveness. Special attention will be paid to the collection of data through contemporary devices, such as sensors.
  • Core concepts that pertain to the integrating and making accessible of large amounts of data, especially the storage and retrieval of data in data warehouses, NoSQL storage infrastructures and data spaces. 
  • The analysis of data with contemporary techniques, such as data mining, machine learning, and statistical analysis tools.
  • The role of domain knowledge in the integration of data and the development of new applications.
  • The provision of platforms/tools (data collection, analysis and visualisation techniques) to the user for the generation of their own applications, such as mashups.
  • The use of methods and/or techniques for the visualisation of information which renders new insights in complex issues to a target group.
  • The realisation of the projects in this exchange programme requires cooperation between disciplines from different fields, including: information technology, mathematics/statistics, information law, graphical design, and interactive media.


This exchange programme consists of lectures combined with assignments connected to the lecture topics. Each period of the exchange programme also contains a practical project, where students work in small project groups to create an innovative solution to a data science problem.

Type of assessment

Theory courses are tested in formal exams after each teaching period of ten weeks. Each theory course has an exam and a resit opportunity. Resits can only be taken if the exam grade was below 5.5.

Practical courses are examined through project assessment with a group and individual component based on the process throughout the project and the delivered product. If the project grade is below 5.5 the student has the opportunity to retake (part) of the practical assignment in consultation with the lecturer.

Each passed course results in receiving the ECTS connected to the specific course.

Learning outcomes

If you have successfully completed this exchange programme then you are able to:

  • Utilise and integrate data from different public sources for the purpose of creating added value for an organisation.
  • Create added value for organisations through the use of grea amounts of complex (open) data.
  • Apply methods, techniques, and tools to interrelate (semi-)structured data sources and make them accessible for the creation of innovative applications.
  • Use domain knowledge in the development of new applications.
  • Analyse data aspects and relate them to domain knowledge, and use the outcomes in your recommendations and the further development of up-to-date knowledge and information systems.
  • Acquire knowledge and understanding
  • Apply knowledge and understanding
  • Make informed judgements and choices
  • Communicate knowledge and understanding


Period 1     
Week 2  - 8

Introduction to the programme, assigning project groups and project assignments, introductory lectures. Courses this period are:

  • Statistics
  • Business and data visualization
  • Data Science project 1
Week 9 Exams
Week 10 Preparations period 2
Period 2 
Week 1 - 7

Introduction to this period, assigning project groups and project assignments, introductory lectures. Courses this period are:

  • Ethics
  • Data mining
  • Data Science project 2
Week 8 Exams
Week 9 Resits period 1
Week 10 Extra week to fix small things, only with lecturers’ permission


After completing your exchange programme at Rotterdam University of Applied Sciences, you will receive a:

  • Transcript of records


An indication of the modules you can expect


Where you can find us
Foto van locatie Location

Wijnhaven 107

Wijnhaven 107 3011 WN

We use analaytics and marketing cookies to improve the website.

Change cookie settings