Your presentation should take into account what the audience will take away from your presentation – a report, an executive summary, or an interactive dashboard.

You can just present the data itself in the various formats while communicating your results.

Generative AI can help with privacy issues in data analytics by generating synthetic datasets with the same statistical properties as the original data.

Analyzing and interpreting the results of your analysis has nothing to do with your initial research questions.

Generative AI can be a useful tool in your data analysis pipeline in suggesting appropriate data visualizations.

Applying your model to unseen test data can help understand how many mistakes your model makes.

Data Visualizations are unimportant in communicating your results to the audience.

Understanding the expectations of your audience is an important step in communicating your results.

It is important to examine your results critically to test the validity of your results.

The interpretation stage of the data analysis pipeline is about exploring the data to create different visualizations.