Course Description 

Managing a data science project requires more than technical expertise. It demands clear problem definition, structured workflows, strong governance, and the ability to communicate value and risks to decision-makers. This course provides participants with practical tools and frameworks to manage data science projects across their full lifecycle, from initial problem framing to deployment and ongoing monitoring.

Participants will learn how to translate business objectives into well-defined data problems, select appropriate success metrics, and organize work using established methodologies such as CRISP-DM and Agile or Scrum. The course emphasizes governance practices that ensure transparency, reproducibility, and accountability, including data documentation, versioning, and workflow tracking.

The module also introduces key concepts related to deploying and maintaining data-driven solutions, including the basics of MLOps, model monitoring, and drift detection. Throughout the course, participants will focus on effective communication, learning how to present results, risks, and return on investment to non-technical and executive audiences.

Through hands-on exercises and collaborative tools, participants will produce concrete project artefacts that reflect real-world data project management practices. By the end of the course, they will be equipped to coordinate technical teams, align data initiatives with organisational goals, and ensure that data science projects deliver sustainable business value.

Course Content 

Learning Outcomes 

• Frame a Data Problem: Define business objectives and success metrics 
• Apply Project Methodologies: use CRISP-DM and Agile/Scrum to plan and track tasks 
• Ensure Governance: implement data governance and documentation systems 
• Plan Deployment and Monitoring: understand MLOps basics and model drift 
• Communicate strategically: present results, risks, and ROI to executive audiences

Practical Work 

• Create a Project Charter using collaboration tools (Miro, Google Docs) 
• Set up a Kanban project board (Trello, Asana) 
• Document data sources and transformations 
• Develop an executive presentation for non-technical audiences

Deliverables 

• Project Charter Document: Goals, scope and initial data requirements 
• Kanban Project Board: live project board demonstrating task management 
• Data Log: shared document tracking data sources and transformations 
• Executive Project Summary Deck: Business Value, Model Risks, and Monitoring Plan. 

Target Audience: 

Managers, project coordinators, and professionals wishing to understand the full lifecycle of managing a data project from conception to deployment 

Prerequisite 

Basic Understanding of data concepts (Data Science recommended) 

Trainer

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 3915, 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. 

Questions: 

Yavuz Sarikaya - Programme Manager 

 yavuz.sarikaya@ulb.be