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Data-Driven Decision Making

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Course Description 

This course is designed for non-technical professionals who want to confidently engage with data science without learning to code. Participants will gain a practical understanding of the full data science lifecycle, from framing business questions and exploring data to interpreting models and translating results into clear, actionable decisions.

Through hands-on exercises using industry-standard no-code and low-code tools, learners will explore real datasets, build dashboards, and interpret predictive modelling outputs. Special emphasis is placed on ethical considerations, bias awareness, and effective communication of insights to stakeholders.

By the end of the course, participants will be equipped to collaborate effectively with technical teams, critically assess data-driven recommendations, and use data as a strategic asset in their organization.

Course Content 

Day 

Content 

1

Data Science Foundations: Definitions, CRISP-DM methodology, data types (structured/unstructured, categorical/numeric) 

2

Exploratory Data Analysis: Visualization principles, common pitfalls. Dashboarding with no-code tools. 

3

Modeling Fundamentals: Features, classification, regression. Ethics, bias, and reproducibility 


Learning Outcomes 
  • Explain the core concepts involved in a Data Science Project (data, storage, analysis, predictive modeling 
  • Visualize different data types using industry-standard no-code tools 
  • Compute descriptive statistics (mean, median, correlation) on tabular datasets 
  • Understand the ethical implications and possible causes of bias in data analysis. 
Practical Work 
  • Data exploration using Excel/Google Sheets (filtering, sorting, pivot tables) 
  • Dashboarding and visualization using Tableau Public, Power BI or Google Data Studio
  • Predictive modeling use cases and guided results interpretation 

Deliverables 
  • Exploratory Data Analysis Summary, executive summary (3-5 points) of findings
  • Interactive Insight Dashboard, a working low-code/no-code dashboard 
  • Decision-Making Memo, a one-page non-technical memo with actionable recommendations 


Target Audience

Non-technical professionals from public or private organisations, including managers, communication specialists, project coordinators, and innovation officers

No prerequisites


Speaker

Jacopo De Stefani serves as a Lecturer at SustAIn.Brussels, bringing a rigorous background in Computer Engineering and Data Science to the program.

As his primary working activity, he is an Expert Data Scientist at Euroconsumers/Test-Achats, supporting business decisions to improve consumer right protection all over Europe.  His past career includes significant tenures in academia as a Machine Learning Researcher and Lecturer at ULB and TUDelft.

He specializes in applied forecasting, having developed solutions for the finance and power systems sectors.  He leverages this extensive cross-industry expertise to provide students with a deeply practical, research-driven and hands-on education.

Price 

Thanks to the support of the European Commission and Innoviris in the framework of the EDIH sustAIn.brussels, SMEs and midcaps receive this training free of charge (0€),  in the context of de minimis aid. Large companies and participants without a company pay 3 915, 09€ per participant.

Practical Information

Language : English (Bilingual exchanges FR/EN welcome) 

Location: BeCentral, Cantersteen 12, 1000 Brussels. 

Format: In person, interactive, hands-on. 

Participants: Max 18 participants. 

Duration: 12 hours total over 3 days. 


Questions

Yavuz Sarikaya - Programme Manager 

 yavuz.sarikaya@ulb.be