AI is changing business — but you don’t need to build it to use it wisely.

This minor is about making better business decisions using data and AI, not about becoming a programmer. Students with or without a technical background learn how to understand data, interpret insights, and apply them to real business problems.

Organisations increasingly recognise that smart use of data — in areas such as finance, marketing, logistics, production, and HR — can significantly improve performance. In practice, however, becoming truly data-driven is challenging. Managers often struggle to understand what data science can offer, while technical specialists may find it difficult to translate insights into actionable business decisions.

During this minor, you learn how to bridge that gap. You develop the skills to connect data, analytics, and AI tools to concrete business and cross-cultural challenges. Step by step, with clear guidance, you gain confidence in using modern analytics tools to support sound decisions in any business role — from marketing and operations to strategy and management.

Real business projects & industry experts

From the very start of the minor, you apply what you learn by working on a real-life business project. Modules are developed and supported by experienced professionals and data scientists, ensuring a strong link between theory and practice.

What you will learn (guided and applied)

Foundation phase (10 weeks)
You are supported in building the foundations needed for data-driven decision-making:

• Understanding how data supports business decisions (CRISP-DM)

• Interpreting data using statistics and visualisation

• Storytelling with data

• Decision intelligence and Ai Agents

• A guided introduction to modelling and analytics
  (No prior programming experience required — support is built in)

Project & tools phase (approx. 16 weeks, starts in the Foundation phase)
You work on an individual business project of your choice, applying selected tools and techniques such as:

• Data analysis and visualisation

• Forecasting and simulation

• Process and data mining

• Market segmentation and customer analysis

• Text and sentiment analysis

• Artificial Intelligence and Machine Learning (applied, not theoretical)

The focus is always on insight, interpretation, and decision-making, not on technical depth for its own sake.

Objective of the minor

The key objective of the minor Data-Driven Decision Making in Business (3DMiB) is to prepare students from economics and business-related programmes to effectively work with data and AI in modern organisations.

Organisations across all sectors increasingly rely on data to support decisions. However, creating value from data requires more than technical expertise alone. Managers must understand what data and analytics can contribute, while data specialists need to translate insights into meaningful business actions.

This minor is not designed to train students as data science specialists or programmers. Instead, it provides students with a practical, intuitive understanding of commonly used data analytics and AI tools, and—more importantly—how their outcomes can be interpreted, evaluated, and used in business decision-making.

The emphasis is on application in organisational contexts. Students learn how to critically analyse results, communicate insights clearly, and support implementation in real business environments.

A central goal of the minor is to bridge the gap between decision-makers and data scientists, enabling effective collaboration between business and technical professionals.

Finally, the minor equips students with the ability to learn how to learn. Rather than mastering specific tools in isolation, students develop the confidence and mindset to navigate the rapidly evolving landscape of data analytics and AI throughout their careers.

Learning outcomes

Upon successful completion of the minor, students will be able to:

1. Analyse business problems by breaking them down into manageable components, identifying relevant data, and using descriptive models to support understanding and decision-making;

2. Translate business questions into data-informed problem statements, suitable for technology-supported analysis, while remaining focused on business relevance rather than technical complexity;

3. Interpret and evaluate analytical outcomes, critically assessing their implications, limitations, and value for organisational decision-making;

4. Communicate data-driven insights effectively to different stakeholders, including non-technical decision-makers;

5. Collaborate effectively with data specialists, demonstrating an understanding of both business and analytical perspectives.

Competences developed

Students completing the minor will demonstrate:

• A foundational understanding of data analytics and AI in business contexts

• The ability to formulate and assess data-driven business problems

• The capability to bridge communication between managers and data professionals

• Experience working in multidisciplinary teams across different study backgrounds

Entry requirements

This exchange minor is most suitable for students who want to strengthen their ability to make business decisions using data and AI, and who are looking to broaden or deepen their professional profile without overlapping with their major.

For whom?

This minor is open to students from the following study programmes:

• International Business

• Logistics Management

• Bedrijfskunde

• Marketing (Commerciële Economie)

• Finance & Control (Bedrijfseconomie)

• Technische Bedrijfskunde

• Other business-related study programmes

The minor is intended for third-year students or higher who are motivated to apply data-driven thinking to real business challenges.

An interest in business domains such as international business, logistics, finance, marketing, or operations is expected. Students should be willing to collaborate in a project-based, team-oriented environment.

Admission requirements

• A good command of English (minimum B2 level; comparable to HAVO English)

• A general business background from your major

• Willingness to work with data, numbers, and digital tools in a supported learning environment

• Motivation to engage actively in group projects and applied assignments

No prior programming experience is required. The minor is designed to support students with different levels of technical experience.

Capacity and selection

The minor has a maximum capacity of 30 students per semester.
If the number of applications exceeds the available places, allocation will be determined by lottery after the enrolment period has closed.

The minor uses state-of-the-art literature from the field of data science and data-driven decision-making, including academic articles, professional publications, and applied workbooks.

All required literature is provided during the minor and is continuously updated to reflect current developments in analytics, AI, and business practice. The focus is on practical applicability and decision-making, rather than purely technical or mathematical depth.

Schedule and working methods

This exchange minor is offered as a full-time programme and follows the academic schedule of HAN International School of Business (ISB).

Schedule

The minor runs over one semester, consisting of two consecutive periods of 10 weeks.

Students are expected to be available on campus from Monday to Friday, between 08:45 and 17:30. As a result, combining this minor with other courses or a part-time job during weekday office hours is not recommended.

Business project activities may take place either:

• At the HAN-ISB campus, or

• At the location of external business partners, depending on the nature of the project.

Working methods

The minor uses a practice-oriented and interactive learning approach, combining different teaching and learning methods, including:

• Applied IT simulations

• Business case teaching

• Interactive lectures

• Supervised consultancy and coaching hours

• Project-based learning

• Guest lectures from industry professionals

• Individual and group project work

The emphasis throughout the minor is on learning by doing, applying concepts to real business challenges, and developing confidence in data-informed decision-making.

Assessment methods

The assessment methods used in this minor include:

Individual learning and reflection

• Portfolio assignments

• Self-assessment and peer assessment

• Case analyses

• Written examinations (focused on concepts and application)

Applied and collaborative work

• Group project (real-life business case)

• Individual project component

• Presentations and role-playing exercises

• Case-based assignments

Applied use of tools

• IT-supported applications and simulations used to analyse business problems
  (assessment focuses on interpretation and outcomes, not coding complexity)

Assessment approach

Across all assessments, students are evaluated on their ability to:

• Understand and structure business problems

• Apply data and analytics tools appropriately

• Interpret results critically

• Communicate insights clearly to different stakeholders

• Reflect on their own learning and collaboration in teams

For content information:
Oliver J. Ntenje, Dennis Moeke, or Mr Witek ten Hoven
E-mail: minor.datadriven-decisionmaking@han.nl

website: Data-Driven Decision Making in Business (hanuniversity.com)

How to subscribe – good to know Registration and allocation

• Minors starting in September
  After the registration period in March, a lottery will take place in April if the number of applicants exceeds the available places.

• Minors starting in February
  After the registration period in October, a lottery will take place in November if the number of applicants exceeds the available places.

If places are still available after the lottery, registration remains open until the subscription period closes.
Please note: once the minor reaches full capacity, registration will be closed immediately.