CODED × KJO · Kuwait
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AI-Powered
Data Analysis

Three days from a raw file to a recommendation you can defend. You will use Copilot and Claude to do in hours what used to take days.

3 days · 6 hours each Finance · Planning · HR · Operations Arabella Hotel
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the three days ↓
The programme

Three days, file to decision.

Day one you learn to work the tool, asking properly, and catching the answer that looks right and isn't. Day two you turn your own department's data into a chart that makes its point. Day three you put a recommendation in front of the room and defend it. Each day ends holding something the next one needs.

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Day 1 6 hours
Day 01

Working With AI on Real Data

Ask it properly, and check its work

Copilot and Claude as analysis partners. What each one is good at, how to prompt them for data work, and how to catch the answer that looks right and is not.

  • AI as your analysis partner: Copilot and Claude, and what each is good at
  • Prompt engineering for data work: asking, refining, and catching errors
  • Hands-on: using AI to summarize, clean, and explore a sample operational dataset
OutcomeParticipants use AI tools confidently for data exploration and leave with a working dataset to build on.
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Analyze
Day 2 6 hours
Day 02

From Data to Insight

Find the pattern, then show it

Copilot inside Excel for the analysis itself. Then reading what the numbers actually say: trends, outliers, and the chart that makes the point without a caption.

  • Copilot in Excel: automated analysis, formulas, and pivot tables
  • Reading the data and telling its story: pattern recognition, choosing the right chart, spotting trends and outliers
  • Department breakout: applying AI to your own department's data to produce a first visualization and interpretation
OutcomeParticipants produce a first working analysis and visualization from their own department's data.
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Recommend
Day 3 6 hours
Day 03

From Analysis to Recommendation

Make the ask, and defend it

Situation, evidence, so-what, the ask. This is the structure a decision-maker can act on. It also covers responsible AI use with organizational data, and a capstone the room presents.

  • Structuring recommendations: situation, evidence, so-what, the ask
  • AI governance and responsible use in Oil & Gas and the public sector
  • Capstone: teams present their department scenario, analysis, and recommendation
OutcomeParticipants present AI-assisted analysis and a recommendation, with a practical plan for ongoing adoption.
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By the end

What you will be able to do.

  • Use Copilot and Claude confidently as analytical partners for data exploration, cleaning, and interpretation
  • Apply effective prompt engineering to get accurate, useful outputs from AI tools
  • Analyze departmental data to identify trends, patterns, and outliers
  • Build clear data visualizations that communicate findings effectively
  • Structure and present a defensible, evidence-based recommendation to decision-makers
  • Apply responsible AI governance practices when working with organizational data