AI Meets Data

Data is everywhere — AI is your new analyst

Every day you work with data. CRM exports, Google Analytics, sales reports, spreadsheets from the finance department. The problem is not that you lack data — the problem is that you cannot quickly get answers from it. Until now, you needed an analyst, SQL or Python. Now you just describe what you want to know, and AI answers.

AI tools like ChatGPT and Claude have improved dramatically over the past two years when it comes to working with data. They understand tables, generate SQL, visualize trends and write analyses — all based on natural language descriptions. You need zero programming knowledge.

What AI can do with data

AI today handles a wide range of data tasks that previously required specialized knowledge or tools:

• Analyze CSV, Excel and other tabular data uploaded directly into the chat
• Generate SQL queries from natural language descriptions
• Clean data — deduplication, format correction, filling missing values
• Create charts and visualizations from your data
• Write formulas for Excel and Google Sheets
• Interpret A/B test results and statistical analyses
• Automate recurring reports and analyses

First steps: ask AI about your data

The simplest way to start is to take data you already have and ask AI a question. You do not need to format, clean or prepare the data. Just copy or upload it and ask.

Uploading CSV to ChatGPT and Claude

ChatGPT (with Code Interpreter) and Claude both allow uploading files directly into the conversation. Upload a CSV or Excel file and AI will automatically read it, understand the structure and let you ask questions about the data. This is by far the most effective way to work with data in AI.

When you upload a file, AI will typically say something like: 'I see a table with 1,247 rows and 8 columns. The columns are: date, product, region, revenue, cost...' And then it asks what you want to find out. Just answer in natural language.

# Example conversation after uploading a CSV

User: [uploads sales_2025.csv]

AI: I see a CSV file with 1,247 rows and 8 columns:
- date (transaction date)
- product (product name)
- region (area)
- revenue (revenue in USD)
- cost (costs)
- quantity (number of units)
- channel (sales channel)
- salesperson (sales rep)

What would you like to find out?

User: Which product has the highest margin and how 
      does it differ by region?

AI: [runs analysis, creates table and chart]

How to formulate data questions

The quality of answers directly depends on the quality of the question. Good data questions are specific, measurable and contextual. Bad questions are vague and unspecific.

• Bad: 'Analyze this data' — AI does not know what you care about
• Good: 'Which 3 products generate the most revenue and how does their share change month over month?'
• Bad: 'Is the data okay?' — too vague
• Good: 'Are there duplicate records in the data? Does any column have more than 5% missing values?'
• Bad: 'Make a chart' — what chart? From what?
• Good: 'Create a bar chart comparing monthly revenue by region for Q1-Q4 2025'

AI tools for working with data

Each of the major AI tools has its strengths for data work:

• ChatGPT Code Interpreter: file uploads, Python analysis, chart generation — ideal for ad-hoc analysis
• Claude: long context (200K tokens), artifacts for visualizations, excellent reasoning — ideal for complex analyses
• Google Gemini: native integration with Google Sheets and BigQuery — ideal for the Google ecosystem
• Microsoft Copilot: integration with Excel and Power BI — ideal for the Microsoft ecosystem
• GitHub Copilot: SQL and Python code generation — ideal for those who already code

Data security

What you will learn in this course

This course will walk you through the entire process of working with data using AI. We start with SQL queries, move to spreadsheets, visualizations, data cleaning, analysis patterns and finish with reporting automation. Every lesson is practical — you will work with real data and real scenarios.