What Conversational AI Can and Cannot Do With Your Data
From the course No-Code Data Analysis with AI: ChatGPT, Excel and SQL for Non-Programmers
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A chart can look impeccable and, at the same time, be completely wrong. You get it in a few seconds by asking the AI for a visualization, you drop it into a presentation for leadership, and if a faulty aggregation sits behind it, nobody will challenge it anymore — because it looks convincing. This is the real stake of data analysis with AI: not the speed at which you reach a result, but the ability to know whether that result is true. The very same tool that can make you several times more productive can also lead you, with utter serenity, to a wrong conclusion. The difference between the two lies in what you know to ask of it and what you know to verify — exactly what we build in this course.
Why this lesson comes first
Most materials about "data analysis with AI" start with an impressive trick: you upload a file, ask for a chart, get a chart, applause. The problem is that the initial enthusiasm masks precisely the risks that matter in a professional context. A beautiful chart generated from a wrong aggregation is more dangerous than no chart at all, because it looks convincing and ends up in a presentation for leadership, where nobody has time to challenge it anymore.
So our pedagogical contract is simple and firm:
- AI accelerates the mechanical work of cleaning, describing, and visualizing data.
- AI does not guarantee the correctness of the results it produces.
- The final responsibility for the numbers remains yours, no matter how articulate the answer sounds.
You will notice throughout the course that we work exclusively with synthetic data — invented datasets, with no real people — precisely so we can experiment freely, without exposing confidential information. This is not a cosmetic detail, but a professional rule, as you will see in the section about GDPR. All the example files in this course (for instance synthetic_orders.csv) are fabricated specifically for learning and do not correspond to any real person or company.
Who this course is for
The course is built for non-programmers: people who work with data every day — in sales, marketing, operations, administration, operational finance — but who do not write code and do not want to learn a programming language just to do their job. You do not need knowledge of Python, R, or advanced SQL. You need two things: curiosity about what your data says and the discipline to verify what the AI answers. The first one you already have, since you are here; the second one we build together.
What conversational AI does WELL
There is a clear category of tasks where an AI assistant like ChatGPT (with the GPT-5.6 Sol model in 2026) saves you entire hours of repetitive work. We group them into four zones of real competence.
1. Fast data cleaning
Real-world data is dirty: extra spaces, inconsistent date formats, values written as "YES/Yes/yes", chaotically named columns, numbers stored as text. AI excels at proposing and executing cleaning transformations, especially when you describe the problem in natural language.
Example prompt (synthetic data):
"I uploaded a CSV file with 1,200 synthetic orders. The
'payment_status' column contains the values: 'paid', 'Paid', 'PAID',
'settled', 'unpaid', 'Unpaid'. Normalize these values into exactly two
categories: 'Paid' and 'Unpaid'. Show me the mapping table before you
apply it and tell me how many rows fall into each category."
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What's next in this lesson
- 2. Descriptive statistics
- 3. Visualizations from a prompt
- 4. Formulating queries
- What AI does POORLY or RISKILY
- 1. Complex calculations "in its head"
- 2. Aggregations on large datasets
- 3. Causal inferences
- 4. Confidential and personal data
Everything you'll learn in this course
1 Fundamentals: What AI Can and Cannot Do with Your Data 3 lessons
- What Conversational AI Can and Cannot Do With Your Data Reading now 50 min
- The Verification Mindset: Why You Never Blindly Trust AI Output 50 min
- The 2026 Tool Landscape: ChatGPT, Copilot, Gemini, and When to Use Each 50 min
2 ChatGPT Advanced Data Analysis: Upload, Cleaning, Statistics and Charts 3 lessons
- Your First File in ChatGPT: Upload, What the Model "Sees", and the Real Limits 56 min
- Cleaning data with prompts: missing values, duplicates, inconsistent formats 57 min
- Descriptive statistics and charts from prompts: mean, distributions, trends 58 min
3 From Business Question to Insight: Formulating Requests on Tabular Data 3 lessons
- The Good-Question Framework: From "How's the Business Doing" to an Analyzable Request 55 min
- Data Prompts That Work: Structure, Context, Anti-Ambiguity 53 min
- Iteration and follow-up: refine the result in dialogue, without starting over 51 min
4 Copilot in Excel: The COPILOT() Function, Natural-Language Formulas and Pivots 3 lessons
- Copilot in Excel, honestly: what it is, what it does and why it is NOT free 54 min
- The COPILOT() Function in a Cell: Summaries, Classifications, and Extractions from Natural Language 56 min
- Natural-Language Formulas, Pivots, and Insights with Copilot — and What You Always Verify 55 min
5 The Accessible Alternative: Excel Analysis Directly in ChatGPT (No Copilot License) 2 lessons
- No Copilot license? Analyze the same Excel file directly in ChatGPT 54 min
- The Excel ↔ ChatGPT workflow in practice: export, analyze, reimport, and reuse 53 min
6 Introduction to Text-to-SQL for Non-Programmers 3 lessons
- What a Database Is and Why SQL Matters (Even If You Never Write It) 53 min
- Text-to-SQL: How a Natural-Language Question Becomes a Query 57 min
- When Text-to-SQL Is Reliable and When It Is NOT: Pitfalls, Ambiguity, and Verification 54 min
7 Verifying AI Output: How to Catch Invented Columns and Wrong Calculations 3 lessons
- Anatomy of a Data Hallucination: Invented Columns, Aggregations, and Hidden Assumptions 56 min
- The 5-step verification protocol: sanity check, sample, recalculation, counter-question 57 min
- The limits of the models and human responsibility: when NOT to use AI on data 52 min
8 Confidentiality and Compliance: What Data NOT to Put in Cloud Tools 3 lessons
- GDPR in a nutshell for AI data analysis: what personal data is and why it matters 54 min
- Public tier vs Enterprise: DPAs, data retention, and how NOT to train the vendor's model "for free" 55 min
- Synthetic Data and Anonymization in Practice: Work Safely, Anytime 53 min
9 Applied Project: A Complete Mini Business Report, from Raw Data to Verified Insight 3 lessons
- The Project Brief and Synthetic Data: Preparing an "Orders + Clients" Dataset 54 min
- Executing the Analysis: Cleaning, Statistics, Segmentation, and Charts End-to-End 58 min
- Verification, the Final Report, and the Course Recap: A Trustworthy Deliverable 56 min
10 Appendix: Official Resources, 2026 Updates and Learning Paths 1 lessons
- Official Resources, 2026 Updates, and Learning Paths 35 min
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Good to know before you start
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