Facts and figures
The programme at a glancePart of School
Programme information
A view of the study programmeThis exchange programme has 5 different subjects. Four of those are regular courses and the fifth is a big project that lasts the entire duration of the semester.
Within the exchange programme there will be different examination methods that will reflect the goals of each subject.
The following methods will be used:
|
Method |
Explanation |
|
Lectures and Written exams |
To provide and check basic knowledge and theoretical understanding. Lectures will deliver core content, while written exams will assess understanding and retention of this knowledge. |
|
Assignments |
To check the skills and understanding taught in the course. |
|
Group Discussions and Presentations |
To improve communication skills and the ability to articulate ideas clearly. Group discussions will encourage collaborative learning and critical thinking. Presentations will allow students to practice and demonstrate their presentation skills. |
|
Projects |
To apply theoretical knowledge to practical scenarios and develop problem-solving skills. Hands-on activities and projects will provide experiential learning opportunities, enabling students to engage with real-world problems. |
|
Research and Report Writing |
To develop deep reasoning, analytical skills, and research capabilities. Students will conduct research and compile their findings into reports, demonstrating their ability to investigate given topics and reach conclusions. |
|
Presentations |
The final project will culminate in a presentation and a written report. The presentation will assess students’ ability to explain their project work and solutions, while the report will evaluate their research depth and analytical skills. |
By employing these diverse teaching methods, the programme ensures that students not only gain knowledge but also develop essential skills such as critical thinking, communication and practicial application, all of which are crucial for their academic and professional success.
The minor consists of 5 parts, 4 subjects and 1 project.
The final grade from project will consist of a final Presentation and Report Writing.
For the subjects it will have the following distribution:
- Statistics - exam
- Data Visualization – exam & group presentation
- Data Mining Machine Learning– exam
- Ethics And Privacy – assignment with group work & report
If you have successfully completed this exchange programme then:
- you will be able to create added value by using large amounts of complex data.
- You will be able to work with data (clean, organize and prepare it).
- You will be able to apply methods, techniques, and tools to data sources, and can carry out a statistical analysis of a dataset and understand the underlying relationship between variables.
- You will be able to understand how important domain knowledge is in the field of data science and how you will be able to use your knowledge to navigate different domain studies.
- You will be able to select, apply, and evaluate Machine Learning algorithms.
- You will be able to successfully execute the phases of a Data Science project cycle: define objectives for the problem (domain), data elicitation, data cleaning, exploration/visualization, feature engineering, model selection/evaluation, and communication of results (application or other product).
- You will be able to analyze data aspects and relate them to your domain knowledge, and you will be able to recognize potential data pitfalls, with applications in ethics and privacy.
Fall 2025
This programme will run from September 1st 2025 until February 6th 2026. One semester is divided into two blocks:
Block 1:
- Data Visualisation (4 ECTS)
- Statistics (4 ECTS)
- Data Science Practice I (7 ECTS)
Block 2:
- Data Mining Machine Learning (4 ECTS)
- Ethics and Security (3 ECTS)
- Data Science Practice II (8 ECTS)
After completing your exchange programme at Rotterdam University of Applied Sciences, you will receive a:
- Transcript of records
Subjects
An indication of the modules you can expect
Block 1
-
Statistics (4 ECTS)
Statistics (4 ECTS)
Topics
- Descriptive Statistics
- Probability Theory
- Hypothesis Testing
- Regression Analysis
Learning materials
Materials will be provided by the lecturer
Learning outcomes
Student will be able to analyze the dataset and understand the basics statistics behind the machine learning algorithms.
Type of assessment
Exam 100%
Module code
CMIBOD021T
-
Business and data visualisation (4 ECTS)
Business and data visualisation (4 ECTS)
Topics
- Data analysis
- Data preparation
- Data visualization
- Business intelligence
Learning materials
Materials will be provided by the lecturer
Learning outcomes
Student will be able to prepare and analyze data, with a special focus on a visualization and business applications.
Type of assessment
Exam 70%
Group presentation 30%Module code
CMIBOD022T
-
Data Science Practice 1 (7 ECTS)
Data Science Practice 1 (7 ECTS)
Topics
- Data collection
- Data Cleaning and preparation
- Exploratory Data Analysis
- Model Evaluation
- Model Building
Learning materials
Materials will be provided by the lecturer.
Learning outcomes
Student will be acquainted with a real-life cycle of a data scientist. Moreover, student will be able to work on a real-life problem.
Type of assessment
Group work and presentations
Module code
CMIBOD011P
Block 2
-
Ethics and Security (3 ECTS)
Ethics and Security (3 ECTS)
Topics
- AI fairness
- Data Privacy
- Legal and ethical aspects of data usage
- Responsible AI
Learning materials
Materials will be provided by the lecturer
Learning outcomes
- You can apply R and its libraries to import and transform complex data sets and create visualisations
- You can carry out statistical analyses and applying the correct methods
- You can explain results of statistical analyses and visualisations
Type of assessment
Exam and assignment
Module code
CMIBOD01TO
-
Datamining machine learning (4 ECTS)
Datamining machine learning (4 ECTS)
Topics
- Machine Learning algorithms
- Application of machine learning algorithms
- Algoritm evaluation
- Model and algorithms training
Learning materials
Materials will be provided by the lecturer
Learning outcomes
Students will learn about how to handle big data, moreover they will learn how to build and apply machine learning algorithms.
Type of assessment
Exam 100%
Module code
CMIBOD023T
-
Data Science Practice 2 (8 ECTS)
Data Science Practice 2 (8 ECTS)
Topics
- Data collection
- Data Cleaning and preparation
- Exploratory Data Analysis
- Model Evaluation
- Model Building
Learning materials
Materials will be provided by the lecturer
Learning outcomes
Student will continue working on a real-life problem
Type of assessment
Group work and presentations
Module code
CMIBOD012P
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