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UID:20260525T160915Z - 36954@eu441a.odoo.com
DTSTART;TZID=Europe/Brussels:20260302T090000
DTEND;TZID=Europe/Brussels:20260304T130000
CREATED:20260525T160915Z
DESCRIPTION:<a href="https://www.sustain.brussels/event/data-driven-decisio
 n-making-189/register">Data-Driven Decision Making </a>\nCourse Descriptio
 n This course is designed for non-technical professionals who want to conf
 idently engage with data science without learning to code. Participants wi
 ll gain a practical understanding of the full data science lifecycle\, fro
 m framing business questions and exploring data to interpreting models and
  translating results into clear\, actionable decisions. Through hands-on e
 xercises using industry-standard no-code and low-code tools\, learners wil
 l explore real datasets\, build dashboards\, and interpret predictive mode
 lling outputs. Special emphasis is placed on ethical considerations\, bias
  awareness\, and effective communication of insights to stakeholders. By t
 he end of the course\, participants will be equipped to collaborate effect
 ively 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 methodolog
 y\, data types (structured/unstructured\, categorical/numeric) 2 Explorato
 ry 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\, ana
 lysis\, predictive modeling Visualize different data types using industry-
 standard no-code tools Compute descriptive statistics (mean\, median\, cor
 relation) on tabular datasets Understand the ethical implications and poss
 ible causes of bias in data analysis. Practical Work Data exploration usin
 g Excel/Google Sheets (filtering\, sorting\, pivot tables) Dashboarding an
 d visualization using Tableau Public\, Power BI or Google Data StudioPredi
 ctive modeling use cases and guided results interpretation Deliverables Ex
 ploratory Data Analysis Summary\, executive summary [...]
DTSTAMP:20260525T160915Z
LOCATION:BeCentral\, Cantersteen 12\, 1000 Bruxelles\, Belgium
SUMMARY:Data-Driven Decision Making 
X-ALT-DESC;FMTTYPE=text/html:<a href="https://www.sustain.brussels/event/da
 ta-driven-decision-making-189/register">Data-Driven Decision Making </a>\n
 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 scienc
 e lifecycle\, from framing business questions and exploring data to interp
 reting models and translating results into clear\, actionable decisions. T
 hrough hands-on exercises using industry-standard no-code and low-code too
 ls\, learners will explore real datasets\, build dashboards\, and interpre
 t predictive modelling outputs. Special emphasis is placed on ethical cons
 iderations\, bias awareness\, and effective communication of insights to s
 takeholders. By the end of the course\, participants will be equipped to c
 ollaborate effectively with technical teams\, critically assess data-drive
 n recommendations\, and use data as a strategic asset in their organizatio
 n. Course Content Day Content 1 Data Science Foundations: Definitions\, CR
 ISP-DM methodology\, data types (structured/unstructured\, categorical/num
 eric) 2 Exploratory Data Analysis: Visualization principles\, common pitfa
 lls. 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 (dat
 a\, storage\, analysis\, predictive modeling Visualize different data type
 s using industry-standard no-code tools Compute descriptive statistics (me
 an\, median\, correlation) on tabular datasets Understand the ethical impl
 ications 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 StudioPredictive modeling use cases and guided results interpretatio
 n Deliverables Exploratory Data Analysis Summary\, executive summary [...]
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