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    Data Science

    Exchange programme

    The field of Data Science is rapidly growing, with data playing a central role in almost every company. Therefore, it is important for future software engineers, as well as other young graduates, to understand and learn how to work with data.

    In today's world, technology is an integral part of our daily lives, from simple tasks like grocery shopping to complex professional practices. This technological integration generates vast amounts of data, making data science a crucial skill for students across all disciplines, not just IT.

    Why Choose the Data Science Exchange Programme?

    Comprehensive Skill Development: This minor equips you with essential skills to handle and analyze data, preparing you for data-driven decision-making in your future career. You'll learn about current trends in data science, cutting-edge technology, statistics, and machine learning.

     Real-World Experience: Gain hands-on experience through interdisciplinary group projects that mirror real-world data science challenges. You'll work with peers from various academic backgrounds, enhancing your ability to collaborate and innovate.

    Research and Innovation: Learn how to navigate the research field, approach research papers, and stay updated with the latest advancements. This minor emphasizes the importance of continuous learning, ensuring you remain competitive in the ever-evolving field of data science.

     Practical Applications: Understand how to manage and utilize the growing amounts of data in both corporate and governmental contexts. You'll explore the challenges of data management, database architectures, and the ethical considerations of data privacy.

    Career Readiness: By promoting interdisciplinary learning and practical experience, this minor prepares you to tackle real-world problems and develop innovative applications. You'll become a competitive candidate in the job market, ready to contribute to any organization.

    Join the Data Science Exchange Programme and Become a Lifelong Learner!

    Embrace the opportunity to develop your skills, work on real-life projects, and stay ahead in the data-driven world. This minor will not only enhance your academic journey but also equip you with the tools to excel in your professional career.

    This programme equips students with foundational data science knowledge and skills to effectively navigate data science projects. Through its unique structure of interdisciplinary group work, students will gain hands-on experience in a real-life data science work cycle. The curriculum covers a comprehensive range of topics, from statistics to introductory machine learning.

    Additionally, this programme will make students aware of potential data isssues, including how data can easily be misleading. It will also address ethical and privacy concerns within this emerging field.

    Facts and figures

    The programme at a glance
    number of ECTS
    30
    period
    September - Early February
    tuition fee
    No tuition fee

    Part of School

    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

    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  

       

    Practical matters

    What you need to know

    Location

    Where you can find us

    Wijnhaven 107

    Wijnhaven 107 3011 WN
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