Ever wondered how to seamlessly integrate AI into your data analysis? The future of data exploration is here, and it’s more intuitive than you might think. Read on to discover a transformative tool that’s redefining the boundaries of AI-enhanced research and education.

MIT Sloan Teaching & Learning Technologies is excited to introduce our new guide to ChatGPT’s Advanced Data Analysis feature (formerly called Code Interpreter). Whether you’re an educator, researcher, data enthusiast, or simply curious about the evolving landscape of AI in education, this resource is for you.

What’s Inside?

ChatGPT’s Advanced Data Analysis function allows users to directly upload data to ChatGPT. You can write, test, and run code seamlessly within the platform. Imagine the possibilities: from exploring intricate datasets to crafting code and tackling empirical challenges, all with the backing of AI.

For those who appreciate visual aids, we’ve incorporated a tutorial video led by MIT Sloan PhD student Chuck Downing. Chuck shows how to activate, access, and optimize this feature, covering foundational tasks such as data reading, cleaning, visualization, and regression analysis.

Why Should You Explore This?

The Advanced Data Analysis feature is not just a tool; it’s a game-changer. Unlike standard chats where you might find yourself explaining data intricacies to the AI, this feature lets you upload files directly. The result? Faster, more accurate answers and a streamlined process to achieve your data objectives.

Whether you’re working with text, images, documents, or even audio and video files, Advanced Data Analysis is designed to support a multitude of formats, primarily focusing on data files like .csv and .txt.

Any content shared with AI tools should NOT include any non-public data such as sensitive information (e.g., social security numbers, credit card information, or hiring materials) and personally identifiable information to comply with MIT’s Policies & Procedures and the Family Educational Rights and Privacy Act of 1974 (FERPA). To learn more, see Navigating Data Privacy.

Looking Ahead

As the landscape of data-driven education evolves, staying updated on the latest tools and technologies is key. We encourage our MIT Sloan community, especially faculty members and students, to familiarize themselves with this feature. Even if you don’t see yourself using Advanced Data Analysis firsthand, understanding its potential can offer insights into its application in classroom settings or research projects. It’s not just about personal use; it’s about envisioning broader possibilities for our academic community. With the rapid advancements in AI, staying informed and adaptable is key.

Explore our new resource here: How to Use ChatGPT’s Advanced Data Analysis Feature. Whether you’re diving in headfirst or just looking for an overview, there’s a wealth of knowledge awaiting you.


Jillian Rubman

Jillian Rubman

As a Lead Instructional Designer at MIT Sloan, I design learning experiences for diverse students on campus and around the globe. I’m passionate about education and technology’s potential to enrich learning experiences.