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    Data Science

    Exchange programme

    Facts and figures

    The programme at a glance

    Programme information

    A view of the study programme

    About the programme

    This 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. 

     

    Method

    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.
    Assignments will test simple skills that ask for practical solutions.

    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

    Learning outcomes

    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.

    Calendar

    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)

    Awarding

    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

    Location

    Where you can find us

    Wijnhaven 107

    Wijnhaven 107 3011 WN

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