Hi there! Today, we’ll be talking about summarizing data in exploratory data analysis (EDA). There are two main methods for summarizing data: numerical and visual.
Numerical summaries involve using descriptive statistics to summarize the data. Descriptive statistics can help you understand the central tendency of the data (like the average), the spread of the data (like the range), and any patterns or outliers that might exist.
Visual summaries involve using charts, graphs, and other visual tools to summarize the data. Visual summaries can help you identify patterns, relationships, and outliers that might not be immediately apparent from looking at the numerical summaries alone.
The benefit of descriptive statistics over looking at individual data points is that it allows you to quickly and easily summarize a large amount of data. It’s like reading a summary of a long book instead of reading the entire book – you can quickly get a sense of what the book is about without having to read every single page.
The benefit of using charts to explore data is that as the size of the data grows, it can become overwhelming to look at individual data points. Charts and other visualizations can help you see the patterns and relationships in the data more easily and quickly.
Finally, combining descriptive statistics with visualizations can help you get a more complete picture of the data. Descriptive statistics can help you identify the central tendency and spread of the data, while visualizations can help you see patterns, relationships, and outliers. By combining these two methods, you can gain a better understanding of the data and make more informed decisions when analyzing it.
In conclusion, summarizing data is an important part of exploratory data analysis. By using both numerical and visual summaries, you can gain a more complete understanding of the data and make more informed decisions when analyzing it. So go forth and summarize your data with EDA!