📊 AI for Data Analysis
Learn AI for Data Analysis on AI4AI — short, hands-on lessons with live AI runs, at three reading levels (beginner to expert). Free to start.
What you'll learn
- From Question to Insight: AI as Your Data Analyst (7 min) — ⚡ Modern AI assistants — such as ChatGPT with Code Interpreter (now called Advanced Data Analysis), Claude with tool us…
- Clean & Explore Messy Data with AI (8 min) — Messy datasets share three common problems: missing values (NaN/null cells that break calculations), wrong data types (…
- Natural Language to SQL: From Business Question to Verified Query (8 min) — Natural language to SQL (NL2SQL) lets you ask a database a question in plain English and receive a runnable SQL query. …
- Charts & Storytelling: AI-Driven Visualization and Narrative (7 min) — When presenting data to stakeholders, two decisions matter most: which chart type to use, and what narrative to wrap ar…
- Avoiding Wrong Answers: Hallucinations, Bias, and Silent Errors (8 min) — AI models make three distinct types of numerical errors that analysts must guard against. **Hallucinated numbers** occu…
- Repeatable AI Analysis: Weekly Reports on Autopilot (8 min) — A repeatable analysis pipeline has three parts: a stable prompt template, a data-injection step, and a scheduled trigge…
Lessons
From Question to Insight: AI as Your Data Analyst
⚡ Modern AI assistants — such as ChatGPT with Code Interpreter (now called Advanced Data Analysis), Claude with tool use, or Gemini Advanced — can act as an end-to-end data analyst. You upload a file (CSV, Excel, JSON) or paste a table, ask a question in plain English, and the m…
Clean & Explore Messy Data with AI
Messy datasets share three common problems: missing values (NaN/null cells that break calculations), wrong data types (a price column stored as text, a date stored as a plain integer), and outliers (values so extreme they skew statistics or signal data-entry errors). Fixing thes…
Natural Language to SQL: From Business Question to Verified Query
Natural language to SQL (NL2SQL) lets you ask a database a question in plain English and receive a runnable SQL query. The AI maps your intent to table names, column names, filters, joins, and aggregations. For this to work well you must give the model your schema — the table an…
Charts & Storytelling: AI-Driven Visualization and Narrative
When presenting data to stakeholders, two decisions matter most: which chart type to use, and what narrative to wrap around it. Poor choices — like a pie chart with 12 slices, or raw numbers with no context — lose the audience before the insight lands. You can prompt an AI to ma…
Avoiding Wrong Answers: Hallucinations, Bias, and Silent Errors
AI models make three distinct types of numerical errors that analysts must guard against. **Hallucinated numbers** occur when a model generates a plausible-sounding statistic that has no grounding in the data or sources provided. This happens because language models predict like…
Repeatable AI Analysis: Weekly Reports on Autopilot
A repeatable analysis pipeline has three parts: a stable prompt template, a data-injection step, and a scheduled trigger. The prompt template uses placeholders (e.g., {{week_start}}, {{csv_data}}) that get filled in at run time — this is called parameterization. The data-injecti…