You can use generative AI to assist with data and numerical analysis by helping organize datasets, identify patterns or trends, and generate visualizations like charts and graphs. AI tools can also perform basic statistical calculations, run simulations or even suggest appropriate analytical methods based on the type of data. This can allow you to interpret results more effectively and draw meaningful conclusions, especially when working on research projects, lab reports or business case studies.
Suggestions
Use AI tools to:
 | Evaluate dataset quality |
 | Perform repetitive numerical tasks, look for hidden patterns in datasets and help you understand your data |
 | Create predictive models and forecasts |
- Use AI tools like Tableau or PowerBI to create data visualizations
- Use AI-powered specialty tools like WolframAlpha or Python libraries to solve complex math problems
- Use GitHub Copilot to help with coding and debugging
Cautions
- Current generative AI platforms are not good mathematical tools and may return errors and struggle with simple calculations, solving word problems or completing multi-step math equations
- Don’t inherently trust the data output of AI tools; use AI to generate code only where the correctness of the solution can be independently verified with other specialized tools
- Never share a dataset with an AI tool in a way that would violate the rights or privacy of others
- Data owned by a company or organization, including personal and business information, should never be shared with an AI tool without permission
Evaluating AI-generated data
- Use your critical thinking skills to check the output and make sure it corresponds with what you know about the data. Are the numbers within a reasonable range?
- Cross-check results using a spreadsheet or statistical software
- Verify that the AI is using the correct statistical methods
- Watch for rounding and approximation errors
- Use multiple analytical tools to cross-verify results