Data-based Modeling and Decision-Making

In today's data-driven world, organizations generate vast amounts of information. Success lies in the ability to transform raw data into actionable insights. This course is designed to equip professionals with the skills needed to turn raw data into actionable intelligence.

This course is ideal for professionals looking to improve their skills in data-driven decision-making and predictive analytics, giving them the tools to make better strategic business decisions.

Target audience: Business leaders, managers, and researchers who wish to understand the complexities of data-driven decision-making to enhance their leadership skills and business strategies.

Prerequisite-knowledge: Good programming skills in for example C/C++, Python, or MATLAB.

Course content:

The course covers:

• Overview of data-driven decision-making processes in modern organizations.
• Introduction to machine learning and its significance in data analytics.
• Exploration of information-based learning techniques for predictions and their use cases.
• In-depth exploration of error-based learning techniques used in predictive analytics.
• Practical examples and case studies showcasing error-based learning in real-world scenarios.
• Introduction to Artificial Neural Networks (ANN) and deep learning.
• Explanation of how neural networks are used to model complex patterns and make predictions.

How the course will be conducted:

The course combines theoretical lectures and practical project components (flipped classroom model). Course assignments and project work will be used for examination.

To pass the course you need to:

Completion of all course assignments as outlined in the curriculum.

Presentation of the teachers:

Dr. Payal Gupta
Professor Jaap van de Beek