Best Practices for Doing Exploratory Data Analysis

Exploratory Data Analysis, or EDA, is an important step to understand your data before you start making models. Here are some simple best practices to follow:

1. Know Your Data Types: Start by figuring out what types of data you have. This can include numbers, categories, or ordered things. Knowing this helps you choose the right methods and pictures for your analysis.

2. Create Statistical Summaries: Calculate important numbers like the average (mean), middle value (median), most common value (mode), and how much the data varies (standard deviation). For example, if you have sales data, finding the average revenue can show how well you’re doing.

3. Use Visualization Techniques:

   • Histograms: These are great for showing how numbers are spread out. For example, if you look at a histogram of customer ages, it will show which age groups are the most common.
   • Box Plots: These help you find unusual values (outliers) and see how data is spread out. You might use box plots to show test scores in different classes.
   • Scatter Plots: These are useful for seeing relationships between two things. For example, if you plot how much money you spend on ads against your sales, you can see if there’s a trend.

4. Look for Patterns and Oddities: Search for trends or interesting connections. For example, does spending more on ads lead to higher sales? Also, watch for any strange spikes or drops that might need a closer look.

5. Clean Your Data: Always check for missing data or outliers because these can mess with your results. You can fix this by filling in missing values or removing the outliers if necessary.

By following these best practices, you can build a strong base for your future modeling. This way, your results will be accurate and meaningful!