Deepnote and ChatGPT: AI Redefines Data Analysis

A Revolution in Data Analysis

In the past, analysts often had to write Python code for every step of the data analysis process, from cleaning to visualization. Today, that era seems far behind us thanks to the emergence of a new generation of AI-powered data analysis tools. These modern tools make the process not only faster but also much more accessible to a broader audience.

These platforms can inspect files, clean messy data, write and execute code, generate graphs, and even help build reusable analysis workflows. This means users can focus more on strategic questions rather than repetitive tasks, thus obtaining useful results more quickly.

In this article, we explore five of the best AI tools for data analysis in 2026. Whether you're working with Python, SQL, notebooks, local projects, or connected data sources, these tools can help you transform raw data into actionable insights much more efficiently.

Deepnote: Optimized Collaboration

Deepnote presents itself as an AI-powered data workspace centered around collaborative notebooks. Its Deepnote Agent is designed to understand the context of your project, create a plan, and add, modify, or delete blocks of Python, SQL, and text in the notebook to accomplish multi-step analysis tasks.

The tool also includes AI features for generative analysis, SQL generation, code completion, code editing, data visualization, error correction, and code explanation. Users can ask questions in plain English and adjust the generated work directly in the notebook.

Deepnote also supports custom AI models compatible with OpenAI for enterprise workspaces. It offers Deepnote MCP, which allows tools like Codex, Claude, Cursor, and VS Code to work with Deepnote projects and notebooks.

Best for: AI-powered data analysis, collaborative notebooks, Python and SQL workflows, and teams wanting AI to work directly in their analysis environment.

ChatGPT: The Simplified Analysis Tool

ChatGPT is one of the simplest AI tools for data analysis. You can upload a CSV or Excel file and simply ask it to clean the data, explore trends, find outliers, execute Python-based calculations, create tables and graphs, and summarize key findings in plain English.

In tests, ChatGPT demonstrated its ability to generate dashboard-style results with key indicators, graphs, and a clear summary, without the user having to write the analysis code themselves. The new working experience of ChatGPT goes even further. You can give it files and a final goal, then ask it to analyze the information and produce a report, a spreadsheet, a presentation, or even a website.

GPT-5.6 is also now available in ChatGPT, with Sol, Terra, and Luna available in Work depending on your plan; OpenAI claims that the new model family enhances professional analytics and spreadsheet outcomes.

Best for: Uploading datasets, exploratory data analysis, visualizations, quick insights, and transforming raw data into clear summaries and reports.

Claude Code: A Versatile Tool

Claude Code is not a traditional data analysis platform, but it proves surprisingly useful for data science tasks and report generation. You can place a dataset in your project folder, open the Claude Code terminal interface, and ask it to inspect the data, write Python scripts, execute the analysis, correct errors, generate visualizations, and produce a final report.

In tests, Claude Code created a complete analysis script, executed it on a dataset about student burnout, checked the results, and generated a 146-line Markdown report. This workflow seems easy as Claude Code can read files, edit code, and execute terminal commands directly.

You are no longer limited to the terminal. Claude Code is available through its terminal interface, desktop application, VS Code extension, and the web. The VS Code extension provides inline diffs and plan reviews, the desktop application can run multiple local and remote sessions in parallel, and the web version allows you to delegate tasks from the browser.

Best for: Python-based data analysis, automated data science workflows, reusable scripts, and detailed report generation from datasets.

DataLab by DataCamp: The Integrated AI Assistant

DataLab is an AI-powered data notebook with an AI assistant similar to ChatGPT integrated directly into the analysis workspace. You can attach a data source, explain what you want in plain English, and the AI can generate Python, R, or SQL code, analyze the results, and explain the conclusions.

In tests, by connecting a Google BigQuery data source and asking a simple question about ticket sales, DataLab inspected the data, generated the analysis, created the visualization, and explained the key result to the user.

AI features go beyond chat. You can generate and edit code with prompts, get inline code completion, debug errors, and ask the assistant to correct or explain the code. The generated work remains visible, allowing you to inspect, edit, and rerun it.

In many ways, DataLab resembles Deepnote. Both combine collaborative notebooks, connected data sources, AI chat, code generation, and reporting in one workspace. However, DataLab seems particularly accessible for those learning data analysis through DataCamp.

Best for: Conversational data analysis, beginners in Python and SQL, analysis of connected databases, debugging, and transforming AI-generated work into editable notebooks and reports.

VS Code with Codex: Interactive Analysis

Codex is available through the ChatGPT desktop application, command-line interface (CLI), IDE extension, and cloud/web workflows. The desktop experience is useful for managing longer tasks, the CLI works directly with local files and terminal tools, and Codex cloud can execute tasks in parallel in isolated environments.

However, for data analysis, the Codex extension in VS Code feels particularly natural. You can open your notebooks, Python scripts, CSV files, and reports in the same project, then ask Codex to inspect the context, edit files, execute the analysis, debug issues, and make follow-up changes. The IDE extension can use open files or selected code as context and allows you to review changes directly next to the source.

For me, this makes data analysis more interactive and iterative. Instead of just getting an answer in a chat window, I end up with the actual scripts, notebooks, graphs, and reports within my project.

Best for: Python-based data analysis, notebooks, reproducible projects, debugging, and analysts already working in VS Code.

Final Thoughts

AI has completely changed my approach to data analysis. I no longer need to write every step of cleaning, graphing, or reporting from scratch. I can upload or connect my data, explain the task, and let AI handle most of the repetitive work.

For quick analysis, I would use ChatGPT. Deepnote and DataLab are excellent when I want an AI-powered notebook experience. For larger Python projects and automated reports, Claude Code works very well.

Personally, I find that VS Code with Codex is the most natural for serious data analysis. I can keep my notebooks, scripts, data, and reports in one place and continue to improve the analysis through follow-up prompts.

My only advice is simple: use AI to go faster, but always check the code, calculations, and final conclusions.