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Engineered to elevate your data analysis and quantitative workflows, these tools offer robust solutions for visualizing data, automating code, and conducting complex analyses. This guide aims to deepen your understanding of how these tools can support data-driven tasks, while also shedding light on their limitations and best-use scenarios.
Keeping Pace at MIT Sloan: We’re fast, but technology can be faster! We do our best to update our content to match these changes. However, for the most recent software updates and features, please refer directly to the developer’s website. When in doubt, contact us.
Below you’ll find a curated selection of AI data analysis and quantitative tools that have proven valuable in academic settings. Please note that this list is not exhaustive but offers a solid starting point for exploring the capabilities of these technologies.
IMPORTANT: At this time, we do not have institutional licenses for the AI tools mentioned below. However, the Institute and School are exploring ways to enhance the AI technology options and we’ll provide updates on any changes.
Any content shared with publicly available 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.
