Lesson 1Mon, 6 Jul7:00-8:30 PM
Read Reports Confidently
Understand what numbers mean, what changed, what needs attention, and what not to trust blindly.
Business
Data & AI Fluency for Business Leaders
Read reports, question data, use AI safely, and turn workplace information into decisions.
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Coach ProfileData & AI
Ezra Muinde
Data Scientist At Naivas · Founder of Ubuntu Analytiq · Nairobi, Kenya
Ezra coaches learners using practical data, analytics, and AI systems experience from retail, training, founder work, and live community programs.
13
Mentees
Currently mentoring on Data & AI
80+
Agentic AI
Students trained
150+
Excel & Power BI
Students trained
About Ezra
- Data Scientist at Naivas
- Founder of Ubuntu Analytiq
- Masterminded Phoenix Analytics AI Agents Summit 2025
- Hosted over 6 webinars on Agentic AI
- Panelist at over 7 events
YOUR LEARNING JOURNEY- Session Schedule
Week 1: Reading Data Critically
Lesson 2Wed, 8 Jul7:00-8:30 PM
Spot Data Traps in Reports
Visual tricks, cherry-picked periods, misleading charts, and selective reporting.
Lesson 3Thu, 9 Jul7:00-8:30 PM
Ask Better Business Questions
Move beyond "send the report" to structured inquiry: what changed, why, who is affected, and what is the decision?
| Lesson | Date | Time | What You Will Learn |
|---|---|---|---|
| 1 | Mon, 6 Jul | 7:00-8:30 PM | Read Reports Confidently Understand what numbers mean, what changed, what needs attention, and what not to trust blindly. |
| 2 | Wed, 8 Jul | 7:00-8:30 PM | Spot Data Traps in Reports Visual tricks, cherry-picked periods, misleading charts, and selective reporting. |
| 3 | Thu, 9 Jul | 7:00-8:30 PM | Ask Better Business Questions Move beyond "send the report" to structured inquiry: what changed, why, who is affected, and what is the decision? |
Week 2: Reading Dashboards, Metrics, & Prompt Engineering
Lesson 4Mon, 13 Jul7:00-8:30 PM
Read Dashboards Without Fear
Leading vs. lagging indicators, what deserves attention, red herrings, and when to trust the trend.
Lesson 5Wed, 15 Jul7:00-8:30 PM
Understand KPI Definitions
Why teams report different numbers for the same metric, how to define metrics so everyone aligns, and which definition matters for your decision.
Lesson 6Thu, 16 Jul7:00-8:30 PM
Master Prompt Engineering for Data
Structure prompts that get real answers, not hallucinations. Use AI to summarize, challenge, explain, and avoid blind copying.
| Lesson | Date | Time | What You Will Learn |
|---|---|---|---|
| 4 | Mon, 13 Jul | 7:00-8:30 PM | Read Dashboards Without Fear Leading vs. lagging indicators, what deserves attention, red herrings, and when to trust the trend. |
| 5 | Wed, 15 Jul | 7:00-8:30 PM | Understand KPI Definitions Why teams report different numbers for the same metric, how to define metrics so everyone aligns, and which definition matters for your decision. |
| 6 | Thu, 16 Jul | 7:00-8:30 PM | Master Prompt Engineering for Data Structure prompts that get real answers, not hallucinations. Use AI to summarize, challenge, explain, and avoid blind copying. |
Week 3: Data Quality & Storytelling
Lesson 7Mon, 20 Jul7:00-8:30 PM
Read Metric Arguments Clearly
Why metrics get calculated five different ways, how to define without fighting, and what is measured vs. what matters.
Lesson 8Wed, 22 Jul7:00-8:30 PM
Recognize Data Quality Disasters
Spot duplicates, missing values, wrong definitions, stale data, and errors before they damage decisions.
Lesson 9Thu, 23 Jul7:00-8:30 PM
Turn Data into Clear Decisions
Structure the story, communicate in language leaders can act on, and move from reporting to recommending.
| Lesson | Date | Time | What You Will Learn |
|---|---|---|---|
| 7 | Mon, 20 Jul | 7:00-8:30 PM | Read Metric Arguments Clearly Why metrics get calculated five different ways, how to define without fighting, and what is measured vs. what matters. |
| 8 | Wed, 22 Jul | 7:00-8:30 PM | Recognize Data Quality Disasters Spot duplicates, missing values, wrong definitions, stale data, and errors before they damage decisions. |
| 9 | Thu, 23 Jul | 7:00-8:30 PM | Turn Data into Clear Decisions Structure the story, communicate in language leaders can act on, and move from reporting to recommending. |
Week 4: Application & Sustainability
Lesson 10Mon, 27 Jul7:00-8:30 PM
Build Business Cases from Data
Frame evidence so choice is clear, answer "what if we do nothing?", and connect data to financial outcomes.
Lesson 11Wed, 29 Jul7:00-8:30 PM
Lead Teams with Data
Use metrics to align, not divide. Ask instead of tell, and know when data matters and when judgment takes over.
Lesson 12Thu, 30 Jul7:00-8:30 PM
Apply AI + Prompts to Real Problems
Take a messy business question, use AI prompting to analyze data, challenge outputs, build recommendations, and present.
| Lesson | Date | Time | What You Will Learn |
|---|---|---|---|
| 10 | Mon, 27 Jul | 7:00-8:30 PM | Build Business Cases from Data Frame evidence so choice is clear, answer "what if we do nothing?", and connect data to financial outcomes. |
| 11 | Wed, 29 Jul | 7:00-8:30 PM | Lead Teams with Data Use metrics to align, not divide. Ask instead of tell, and know when data matters and when judgment takes over. |
| 12 | Thu, 30 Jul | 7:00-8:30 PM | Apply AI + Prompts to Real Problems Take a messy business question, use AI prompting to analyze data, challenge outputs, build recommendations, and present. |
ExcelDashboardsChatGPTClaudeGeminiReportsScorecards
Modules
Data and AI fluency for business leaders
Reports, dashboards, and KPI definitions
Prompt engineering for sharper business questions
Data quality traps and AI output stress-testing
Decision storytelling, business cases, and human > AI > human workflows
Start Date
6 July 2026
Classes start 6th July.
Projects
Project 1: The Audit - Spot 5 quality issues in a broken dashboard and propose fixes.
Project 2: The Scenario Model - Build a 3-scenario Excel model and brief leadership on trade-offs.
Project 3: The AI Extraction - Use AI to extract patterns from unstructured data, then validate and frame the limitations.
Project 4: The Business Case - Gather data, structure an argument, and persuade stakeholders to act.
Project 5: The Diagnosis - Decompose a metric drop, identify root causes, and recommend next steps.
Project 6: The AI Strategy Memo - Identify one useful AI workflow, its risks, and the human review needed before rollout.
Market fit
Demand and roles
See where this pathway fits in local teams, markets, and practical work.
Roles
ExecutiveManagerOperations managerSales leadFinance assistantProgram officerAny Leadership RoleBusiness Intelligence Analyst
Demand areas
SMEsNGOsOperationsCustomer supportLeadershipBusiness teamsAnalytics consumers