ChatGPT Advanced Data Analysis on Synthetic or Aggregated Data

Before you touch ChatGPT, generate a fake dataset with the same structure as the one you actually want to explore: same column names, same types, same rough distribution shape, but no real subject, claims, or survey rows. You likely already have a prompt from your Level 1 toolkit for this ("generate synthetic test data"), or you can write a short R or Python script yourself. Save it as a CSV.

What you should see: A CSV file that opens cleanly and looks, at a glance, like your real analysis dataset, minus anything identifiable.

1. Go to chatgpt.com and sign in
2. Start a new conversation
3. Select a model that supports code execution, such as GPT-5.6 Sol, from the model picker
4. Look for an attach or upload icon near the message box (its exact position shifts between updates, but it is always near where you type)

What you should see: After selecting a capable model, the message box shows an option to attach a file. Troubleshooting: If you do not see an attach option, confirm your subscription is active and try refreshing the page. Free-tier accounts do not get file upload and code execution.

1. Click the attach icon and select your synthetic CSV
2. Wait for the upload to finish. Larger files take longer
3. Type a plain-language question about the data
4. Send the message and watch the response build. ChatGPT writes Python behind the scenes, runs it, and returns a chart, a table, or both

What you should see: A short explanation of what was computed, followed by an image (for a plot) or a formatted table, plus an offer to keep exploring. Troubleshooting: If the file fails to upload, check its size. Files well past 50MB tend to time out. Split a large file or aggregate it further before trying again.

Advanced Data Analysis keeps the uploaded data available for the rest of the conversation, so you can ask a second and third question without re-uploading.

1. Ask a follow-up that builds on the first answer ("Now break that same distribution out by the group variable")
2. Request the underlying code if you want to see exactly what ran
3. Ask for the code to be downloadable if you plan to reuse it

What you should see: Each follow-up reuses the same uploaded file unless you say otherwise. Requested code appears in a code block you can copy.

Nothing that comes out of this chat is a result you report. Copy any code you want to keep, paste it into your actual R, Python, or SAS session, and run it against the real analysis dataset there.

1. Copy the generated code into your usual script editor
2. Adjust variable names or paths to match your real dataset
3. Run it in your validated environment and compare the output to what the chat showed on synthetic data

What you should see: The same shape of result (a similar distribution, a similar chart) on your real data, confirming the approach translates.