In this module, you will explore practical interaction patterns for working with documents, structured data, media, and collections of files in ChatGPT Advanced Data Analysis. You will learn how to read and reshape small documents, inspect and visualize structured datasets, transform media files, and automate tasks across multiple files using zip archives. The module also introduces ways to judge task difficulty, especially when working with structured versus unstructured data, small versus large documents, and tasks that require information to be brought into the conversation for more reliable reasoning.

In this module, you will learn how to identify high-value problems for ChatGPT Advanced Data Analysis and apply a practical framework for using AI effectively and responsibly. You will explore how to choose tasks that are easy to verify, benefit from partial progress, and support rather than replace human reasoning. You will also examine the ACHIEVE framework for using AI to aid coordination, reduce tedious work, support creativity and problem solving, provide a safety net, and help strong ideas scale more quickly. Through real examples, you will see how ChatGPT Advanced Data Analysis can support planning, communication, analysis, and follow-through in ways that keep humans actively involved in judgment and decision-making.

In this module, you will learn practical workflows for using ChatGPT Advanced Data Analysis to move from raw files to useful outputs. You will explore the extract–transform–AI–create pattern, learn when to reshape data before analysis, and examine how ChatGPT Advanced Data Analysis can help you generate drafts in a wide range of formats. You will also compare step-by-step prompting with AI planning, learn techniques for refining and saving plans, and use question-driven planning to help the tool build better workflows for more complex tasks.

In this module, you will learn practical strategies for troubleshooting ChatGPT Advanced Data Analysis when tasks do not work as expected. You will explore techniques for prompting alternate methods, steering text analysis away from Python when GPT-4 reasoning is needed, and revising earlier prompts to correct errors more effectively. The module also covers ways to reduce hallucinations, validate outputs using identifiers, quotations, and test cases, and work with large documents and large outputs by breaking them into manageable parts, building indexes, and using outlines to preserve structure and continuity.