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Training
Course Information
Course Name
AI-DADM: AI for Data Analysis & Decision Making
Duration
2 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
Overview
Overview
Audience Profile
Audience Profile
- Management teams, data analysts, business analysts, operations & finance teams, and business users working with data (Excel, CSV, dashboards) with no coding knowledge.
Prerequisities
Prerequisites
At Course Completion
At Course Completion
By the end of the 2 days, participants will be able to:
- understand how AI and large language models (LLMs) support data analysis
- apply structured prompting techniques (RCCT, zero-shot, few-shot, iterative) for analysis
- clean, transform, and explore datasets using AI
- generate insights, trends, and summaries efficiently
- build clear, data-driven narratives for decision-making
- design simple AI-assisted data workflows
- automate repetitive analysis tasks (reports, summaries, insights)
- validate AI outputs and avoid misleading conclusions
- understand AI limitations, risks, and governance best practices
- understand how AI can automate multi-step analysis workflows
Course Outline
Course Outline
Module 1: Understanding AI, LLM & Data Context
- What is AI vs Generative AI
- What is an LLM (in simple terms)
- How AI processes data and text
- Types of data:
- Structured (Excel, tables)
- Unstructured (reports, text)
- Strengths and limitations:
- Hallucination
- Bias
- Context limitations
- AI as assistant, not decision-maker
Module 2: RCCT Prompting for Data Analysis
- Why analysis prompts fail
- Role, Context, Constraints, Task (RCCT)
- Writing clear analytical prompts
- Controlling outputs:
- Tables
- Bullet insights
- Summaries
- Comparing weak vs strong prompts
Module 3: Prompting Techniques for Better Insights
- Zero-shot prompting
- One-shot and few-shot prompting
- Asking analytical questions effectively
- Iterative prompting (refining outputs step-by-step)
- Improving accuracy through prompt refinement
Module 4: Data Preparation & Analysis Thinking
- Common data issues:
- Missing values
- Duplicates
- Inconsistent formats
- Importance of data quality
- Introduction to analysis thinking:
- Trends
- Segments
- Outliers
- Asking the right business questions
Module 5: Presenting Insights Clearly
- Turning data into insights
- Structuring findings:
- What happened
- Why it happened
- What to do next
- Choosing appropriate visuals
- Communicating to decision-makers
Module 6: AI-Assisted Data Workflow
- Workflow thinking:
- Input → AI → Output
- Multi-step analysis using AI:
- Clean → Analyze → Summarize
- Identifying automation opportunities:
- Reports
- KPI summaries
- Data explanations
Module 7: Risks, Validation & Best Practices
- Why AI outputs can be wrong
- Detecting misleading insights
- Validating results
- Human-in-the-loop approach
- Duplicates
- Inconsistent formats
- Importance of data quality
- Introduction to analysis thinking:
- Trends
- Segments
- Outliers
- Asking the right business questions
Module 8: Data Exploration with AI
- Upload dataset (CSV / Excel)
- Generate dataset summary
- Identify key variables and patterns
Module 9: Data Cleaning & Transformation
- Cleaning datasets using AI
- Standardizing formats (dates, categories)
- Removing duplicates and inconsistencies
Module 10: Insight Generation & Analysis
- Identifying:
- Trends
- Segments
- Outliers
- Generating actionable insights
- Comparing AI-assisted vs manual analysis
Module 11: Reporting & Business Insights
- Creating:
- Executive summaries
- Key findings
- Recommendations
- Structuring reports for decision-makers
Module 12: Decision Support & Scenario Analysis
- Using AI to:
- Compare scenarios
- Evaluate options
- “What-if” analysis
- Generating recommendations
- Supporting decisions with data
Module 13: AI + Tools Integration
- Using AI with:
- Excel / Google Sheets
- Generating formulas using AI
- Automating repetitive analysis tasks
Module 14: End-to-End Analysis Workflow
- Full workflow:
- Data → Clean → Analyze → Report
- Building reusable prompt templates
- Multi-step analysis using AI
- Human validation checkpoints
Module 15: Validation & Accuracy Improvement
- Verifying AI outputs
- Identifying incorrect insights
- Improving accuracy with better prompts
- Avoiding over-reliance on AI
