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BEGIN:VEVENT
UID:20260514T205602Z - 86547@eu441a.odoo.com
DTSTART;TZID=Europe/Brussels:20260330T090000
DTEND;TZID=Europe/Brussels:20260401T130000
CREATED:20260514T205602Z
DESCRIPTION:<a href="https://www.sustain.brussels/event/managing-a-data-pro
 ject-190/register">Managing a Data Project </a>\nCourse Description Managi
 ng a data science project requires more than technical expertise. It deman
 ds clear problem definition\, structured workflows\, strong governance\, a
 nd the ability to communicate value and risks to decision-makers. This cou
 rse provides participants with practical tools and frameworks to manage da
 ta science projects across their full lifecycle\, from initial problem fra
 ming to deployment and ongoing monitoring. Participants will learn how to 
 translate business objectives into well-defined data problems\, select app
 ropriate success metrics\, and organize work using established methodologi
 es such as CRISP-DM and Agile or Scrum. The course emphasizes governance p
 ractices that ensure transparency\, reproducibility\, and accountability\,
  including data documentation\, versioning\, and workflow tracking. The mo
 dule also introduces key concepts related to deploying and maintaining dat
 a-driven solutions\, including the basics of MLOps\, model monitoring\, an
 d drift detection. Throughout the course\, participants will focus on effe
 ctive 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 pro
 ject artefacts that reflect real-world data project management practices. 
 By the end of the course\, they will be equipped to coordinate technical t
 eams\, align data initiatives with organisational goals\, and ensure that 
 data science projects deliver sustainable business value. Course Content D
 ay Content 1 Problem Framing & Project Initiation: Business objectives\, C
 RISP-DM deep dive\, stakeholder identification 2 Data Governance\, Workflo
 w & Versioning: Agile/Scrum for data projects\, data lineage\, model versi
 oning. 3 Model Deployment & Monitoring: Proof-of-concept to production\, M
 LOps introduction\, model drift Learning [...]
DTSTAMP:20260514T205602Z
LOCATION:BeCentral\, Cantersteen 12\, 1000 Bruxelles\, Belgium
SUMMARY:Managing a Data Project 
X-ALT-DESC;FMTTYPE=text/html:<a href="https://www.sustain.brussels/event/ma
 naging-a-data-project-190/register">Managing a Data Project </a>\nCourse D
 escription Managing a data science project requires more than technical ex
 pertise. It demands clear problem definition\, structured workflows\, stro
 ng governance\, and the ability to communicate value and risks to decision
 -makers. This course provides participants with practical tools and framew
 orks to manage data science projects across their full lifecycle\, from in
 itial problem framing to deployment and ongoing monitoring. Participants w
 ill learn how to translate business objectives into well-defined data prob
 lems\, select appropriate success metrics\, and organize work using establ
 ished methodologies such as CRISP-DM and Agile or Scrum. The course emphas
 izes governance practices that ensure transparency\, reproducibility\, and
  accountability\, including data documentation\, versioning\, and workflow
  tracking. The module also introduces key concepts related to deploying an
 d maintaining data-driven solutions\, including the basics of MLOps\, mode
 l monitoring\, and drift detection. Throughout the course\, participants w
 ill focus on effective communication\, learning how to present results\, r
 isks\, and return on investment to non-technical and executive audiences. 
 Through hands-on exercises and collaborative tools\, participants will pro
 duce concrete project artefacts that reflect real-world data project manag
 ement practices. By the end of the course\, they will be equipped to coord
 inate technical teams\, align data initiatives with organisational goals\,
  and ensure that data science projects deliver sustainable business value.
  Course Content Day Content 1 Problem Framing & Project Initiation: Busine
 ss objectives\, CRISP-DM deep dive\, stakeholder identification 2 Data Gov
 ernance\, Workflow & Versioning: Agile/Scrum for data projects\, data line
 age\, model versioning. 3 Model Deployment & Monitoring: Proof-of-concept 
 to production\, MLOps introduction\, model drift Learning [...]
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