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Data & AI - The Careers Behind It

Mulham Fetna
Author
Mulham Fetna
Renaissance Engineer
Table of Contents

“Data” and “AI” get used as if they were one job. They are at least four, they need different people, and confusing them is how organizations waste a year and a budget.

This workshop takes the field apart — for people who will use, fund, hire for, and judge data and AI work, not build it themselves.

Format
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4 hours, on-site, highly interactive.

Delivered in 2026 for SyrProNet, the Syrian Professional Network, to an audience of senior professionals — founders, consultants, project managers, academics, and decision-makers.

What we cover
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The four roles, honestly distinguished. Data analysis — what happened. Data engineering — the plumbing that makes everything else possible, and the part almost everyone underfunds. Data science — why it happened and what is likely next. Machine learning and AI — systems that decide or generate, including the ones that fail quietly.

What each one actually needs. The skills, tools, timeline, and realistic cost. What you can expect in three months versus three years.

How to judge the work. The questions to ask a data hire, a vendor, or a consultant that separate substance from theatre. Why “we use AI” is not an answer, and what a good answer sounds like.

Where AI genuinely helps — and where it does not. Including the uncomfortable cases: when the data does not exist, when the problem is organizational rather than technical, and when a spreadsheet is the correct solution.

Who it’s for
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Founders, managers, consultants, academics, and decision-makers who need to be literate, not technical. No coding, no mathematics prerequisite.

Also useful for students choosing which of these paths to actually enter.

Book this workshop
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Available for professional networks, companies, and universities. Get in touch or email contact@mulhamfetna.com.

Common questions
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Do I need a technical background for this workshop?
No. It is built for decision-makers — founders, managers, consultants, and academics — who need to understand and judge data and AI work without building it. There is no coding and no mathematics prerequisite.
What is the difference between data analysis, data engineering, and data science?
Data analysis explains what happened. Data engineering builds the pipelines and infrastructure that make any analysis possible, and is the most commonly underfunded of the three. Data science explains why something happened and what is likely to happen next. Machine learning and AI build systems that decide or generate. These are different jobs needing different people, and treating them as one is a common and expensive mistake.
Who delivers this workshop?
Mulham Fetna, a mechatronics and AI/ML engineer based in Aleppo, Syria. He delivered it in 2026 as a four-hour on-site session for SyrProNet, the Syrian Professional Network, to senior professionals including founders, consultants, project managers, and academics.

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Mulham Fetna
Author
Mulham Fetna
Renaissance Engineer