A smart model is just the beginning. The real challenge? Turning those insights into winning choices. In the Data Driven Decision Making minor, you will learn how to bridge the gap between data science and strategic impact. Go beyond prediction and discover the world of optimization, causal inference, and decision models. Learn how to navigate uncertainty and translate complex data into compelling advice.

In the Data-Driven Decision Making minor, you'll learn how to leverage the power of AI and data science to make better, faster, and more informed decisions. For an AI specialist, an accurate model is only valuable if it leads to the right action. In this minor, the focus shifts from predictive to prescriptive analytics: we look not only at what will happen, but primarily at: "What should we do?"

 The minor consists of the following core components:

 1. From Prediction to Prescription. Most AI models predict an outcome. In this unit, you'll learn the next st

ep: optimization. You'll be introduced to Operations Research and mathematical programming. How does an airline optimally deploy its aircraft? How does a hospital organize its operating rooms? You'll learn to write algorithms that calculate the "best" solution within complex constraints.

 2. Causal Inference: Why do things change? One of the biggest pitfalls in data analysis is confusing correlation with causality. You'll learn advanced techniques (such as structural equation modeling and do-calculus) to determine whether an intervention (e.g., a price change) is actually the cause of an outcome. This is essential for policymakers and strategists

 3. Decision-Making Under Uncertainty. In the real world, data is rarely perfect, and the future is uncertain. You'll learn how to deal with uncertainty through probabilistic programming and simulation models. You'll design decision trees and use Bayesian statistics to continuously adjust decisions based on new information.

 4. Experimental Design & A/B Testing at Scale. How do you test a new strategy in a complex environment? You'll delve into designing experiments. We'll explore large-scale A/B testing, multi-armed bandits, and techniques for continuous optimization without compromising the user experience.

 5. Data Storytelling & Stakeholder Management. A perfect model that isn't understood isn't used. You'll learn the art of data visualization and storytelling. How do you convince a board or government agency of your data-driven advice? You'll learn to translate complex quantitative results into a clear and actionable narrative.

 6. The Ethics of Autonomy. When should you let an algorithm make autonomous decisions, and when should a human be involved (Human-in-the-loop)? We discuss the societal impact of algorithmic management and the dangers of feedback loops, where today's decisions contaminate tomorrow's data.

After completing the Data-Driven Decision Making minor, students will be able to:

• Translate complex organizational issues into quantitative decision models and analytical hypotheses.
• Apply advanced statistical methods and causal inference to distinguish between correlation and causality in decision-making.
• Use prescriptive analysis techniques and optimization algorithms (such as those used in Operations Research) to determine the best course of action in scarcity scenarios.
• Model decision-making under uncertainty using techniques such as Bayesian networks and Monte Carlo simulations.
• Translate data insights into compelling strategic advice for non-technical stakeholders (data storytelling).
• Evaluate and manage the risks and ethical implications of automated decision-making.

Target groups: study programmes in Technology

Requirements: Mathematics A or B (VWO level), programming, AI engineering

Description of meetings: Lectures, discussions, workshops and an ongoing project. 
Attendance required during assessment moments and collaboration in project groups.

Contact hours spread throughout the week during the day; 10 to 15 hours per week

Performance profile, 15 EC, minimum 5,5: The student delivers performance based on established performance indicators. Work attitude (behaviour), professional products (skills) and written test (knowledge) will be assessed.

Locations: Maastricht Paul-Henri Spaaklaan and Heerlen Brightlands Campus