Data Science for Decision-Makers — 3-Day Workshop

3-day executive training: understanding AI, use cases, ROI, steering data projects.

Instructor: Dr. Yaé Ulrich Gaba Duration: 3 days (18 hours) Level: Non-Technical / Executive Language: English

Overview

This workshop is designed for managers, executives, project leads, and non-technical professionals who need to understand AI and data science to make informed decisions. No coding is required. Through case studies, interactive demonstrations, and group exercises, participants learn to evaluate AI opportunities, steer data projects, and ask the right questions of their technical teams.

Prerequisites

  • No technical or programming background required
  • Experience managing teams, projects, or business operations
  • Curiosity about AI and data-driven decision-making
  • Laptop for interactive exercises (browser-based tools only)

Learning Objectives

By the end of this workshop, participants will be able to:

  1. Understand what AI, machine learning, and data science can (and cannot) do
  2. Identify high-value AI use cases in their organization
  3. Evaluate the feasibility, cost, and ROI of data projects
  4. Communicate effectively with data science teams
  5. Understand ethical, legal, and governance considerations for AI adoption
  6. Build a data strategy roadmap for their organization

Day-by-Day Program

Day 1: Understanding AI & Data Science

Objectives: Build a solid conceptual foundation without any code.

Time Topic
09:00–10:00 What is AI, Really? — Demystifying buzzwords: AI vs. ML vs. deep learning vs. generative AI. What machines can learn and what they can’t. Common misconceptions
10:00–10:45 How Machine Learning Works — The intuition behind supervised learning (prediction), unsupervised learning (pattern discovery), and reinforcement learning (optimization). No math, just examples
10:45–11:00 Break
11:00–12:30 The Data Pipeline — Where data comes from, data quality challenges, the lifecycle of a data project: collection → cleaning → analysis → modelling → deployment → monitoring
12:30–14:00 Lunch
14:00–15:30 AI in Action: Case Studies — Real-world examples across sectors: healthcare (disease prediction), finance (fraud detection), agriculture (yield forecasting), retail (recommendation engines), telecom (churn prediction)
15:30–15:45 Break
15:45–17:00 Interactive Demo — Live demonstration of building a simple ML model (no coding by participants). See how data goes in, predictions come out. Understanding accuracy, errors, and confidence

Exercise 1: In groups, identify 3 potential AI use cases in your organization. For each, describe: the business problem, the data needed, the expected impact, and potential risks.

Day 2: Evaluating & Steering Data Projects

Objectives: Learn to evaluate AI proposals, manage data projects, and communicate with technical teams.

Time Topic
09:00–09:30 Exercise Review — Group presentations of use cases
09:30–10:30 Is This Project Feasible? — The feasibility checklist: data availability, data quality, problem complexity, team capability, infrastructure, timeline. Red flags and green lights
10:30–10:45 Break
10:45–12:00 Cost & ROI of AI — Budgeting a data project: team, infrastructure, data, time. Measuring ROI: direct savings, efficiency gains, revenue growth, risk reduction. When AI is not the answer
12:00–12:30 Vendor Evaluation — Build vs. buy decisions, evaluating AI vendors, understanding SaaS vs. custom solutions, contract considerations
12:30–14:00 Lunch
14:00–15:30 Speaking Data Science — The vocabulary you need: features, labels, training, testing, overfitting, precision vs. recall, false positives/negatives. How to read a model performance report. Questions to ask your data team
15:30–15:45 Break
15:45–17:00 Project Management for AI — Agile for data projects, defining success criteria, milestones, managing expectations, the “last mile” problem (from prototype to production)

Exercise 2: Given a fictional AI project proposal (with budget, timeline, and technical approach), evaluate it: Is it feasible? Is the ROI justified? What questions would you ask the technical team? Present your assessment.

Day 3: Strategy, Ethics & Roadmap

Objectives: Build a data strategy and understand governance, ethics, and organizational change.

Time Topic
09:00–09:30 Exercise Review
09:30–10:30 Generative AI for Business — What LLMs can do for your organization: document processing, customer service, knowledge management, content generation. Opportunities and risks
10:30–10:45 Break
10:45–12:00 Data Governance & Ethics — Data privacy (GDPR basics), bias in AI systems, transparency and explainability, accountability, AI policies for organizations, the African regulatory landscape
12:00–12:30 Building a Data Team — Roles (data engineer, data scientist, ML engineer, analyst, product manager), hiring vs. training, structuring a data function
12:30–14:00 Lunch
14:00–15:00 Organizational Change — Cultural readiness for AI, change management, data literacy across the organization, quick wins vs. transformative projects
15:00–15:15 Break
15:15–16:15 Building Your Roadmap — Workshop: create a 12-month data strategy roadmap for your organization. Prioritize use cases, define milestones, identify resources needed, plan for governance
16:15–17:00 Presentations & Wrap-Up — Each group presents their roadmap. Peer feedback, Q&A, certificates

Exercise 3 (Capstone): Deliver a 10-minute presentation of your organization’s AI roadmap, including:

  • Top 3 prioritized use cases with business justification
  • Data readiness assessment
  • Resource and budget plan
  • Governance and ethics considerations
  • Quick wins (0–3 months) and strategic goals (6–12 months)

Assessment

  • Group exercises (50%) — Quality of use case identification and project evaluation
  • Capstone roadmap (30%) — Completeness and realism of the data strategy
  • Participation (20%) — Engagement in discussions and group work

Resources

Certificate

Participants who complete all exercises and the capstone roadmap receive a certificate of completion.