Tips for Exploratory Data Analysis (EDA) in Julius

If you would like a complete guide on how to do exploration / exploratory data analysis (EDA), here is a detailed step-by-step guide: Guide: Data Exploration / EDA in Julius

Here are some tips to make the most out of your data exploration with Julius AI:

  • Start with a clear question or objective in mind. This will guide your cleaning and analysis steps.

  • Strive for clarity by using simple and easily understandable language. Provide detailed explanations when necessary.

  • I recommend breaking down your analysis into individual steps and having Julius present the results of each step. This will allow you to assess whether you are achieving the expected outcomes and if Julius has understood your instructions and made any necessary adjustments.

  • Use visualizations to get a better grasp of your data. Julius AI can generate a variety of plots to help you see patterns and outliers.

  • Be mindful of any missing values in your dataset and determine the best strategy for dealing with them, whether that be imputation or exclusion.

  • Make sure not to overlook any categorical variables in your analysis. Use bar charts or frequency tables to examine their distributions.

  • Remember that data exploration is typically a step-by-step process. Continuously explore various variables and visualizations to obtain a thorough understanding of the dataset.


i really appreciate your point that EDA is an iterative process. its so important to thoroughly explore the data from multiple angles. julius’s natural language capabilities seem like they would be incredibly helpful for describing findings and facilitating that kind of multi-faceted evolving analysis

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Asking the right questions is key to better data analysis. Also, one has to be able to break the problem into logical solution steps to achieve a comprehensive analysis.

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I am new to this community.
Thank you so much

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