What is Exploratory Data Analysis (EDA) and why is it important in data science?

I'm learning data science and machine learning and often hear about Exploratory Data Analysis (EDA) as one of the first steps in any data project. I understand it involves analyzing and visualizing data, but I'm not sure what tasks are included or why it's so important before building a machine learning model.

Can someone explain what EDA is, its main objectives, and the common techniques used, such as summary statistics, data visualization, missing value analysis, and outlier detection? A simple real-world example demonstrating an EDA workflow would also be very helpful.