Facts and figures
The programme at a glancePart of School
Programme information
A view of the study programmeThe program is divided into three blocks: (i) Exploratory Data Analysis, (ii) Machine Learning, and (iii) AI in Data Science. Each block consists of thematic units, with each unit delivered over the course of one week. Units are structured to include theoretical instruction, followed by practical exercises and application within a training project. At the end of the first and second blocks, an additional project is completed in which the acquired concepts are applied.
The examination consists of a multiple-choice test covering the theoretical components, as well as a written report and oral defence related to the project work.
The following methods will be used:
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Method |
Explanation |
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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.
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Assignments |
To check the skills and understanding taught in the course. |
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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.
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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. |
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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.
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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.
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By employing these diverse teaching methods, the program ensures that students not only gain knowledge but also develop essential skills such as critical thinking, communication, and practical application, all of which are crucial for their academic and professional success.
The final grade is determined by the score in the multiple-choice exam (50%) and the project score (50%), consisting of final Presentation and Report Writing.
If you have successfully completed this exchange program 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, recognizing and avoiding potential data pitfalls.
Fall 2026
The program is divided into three blocks: (i) Exploratory Data Analysis, (ii) Machine Learning, and (iii) AI in Data Science. Each block consists of thematic units, with each unit delivered over the course of one week. The structure per block:
- Exploratory Data Analysys
- Introduction to Data Science & Descriptive Analytics
- Data Visualization Basics
- Data Cleaning & Preparation
- Statistics for Descriptive Analytics
- Exploratory Data Analysis
- Regression Models
- catch-up week
- catch-up week
- Machine Learning
- Introduction to Machine Learning & Metrics
- Classification Models
- Artificial Neural Networks
- catch-up week
- AI in Data Science
- Introduction to AI in Data Science
- Automating DS with AI
- Intelligence Amplification in DS
- Catch-up week
The projects work in parallel with the core project starting on week 12 (after substantial theory is obtained) and ending in week 18.
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 and 2
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Data Science (30 ECTS)
Data Science (30 ECTS)
Topics
- Exploratory Data Analysis
- Machine Learning
- AI in Data Science
Learning materials
Materials will be provided by the lecturer
Learning outcomes
Students will be acquainted with a real-life cycle of a data-scientist, allowing them to
- Prepare and analyze data, with a special focus on a visualization and business applications,
- Analyze the dataset and understand the basics statistics behind the machine learning algorithms,
- Apply and evaluate machine learning algorithms,
- Apply learned DS concepts on a real-life problem.
Type of assessment
Multiple-choice exam (50%)
Group work and presentations (50%)Module code
CMIBOD021T
Practical matters
What you need to knowLocation
Where you can find us
