Data Exploring

Data exploring focuses on understanding data quality, patterns, distributions, and relationships.

There are several data exploration techniques. The most common ones are:

• Heading data (looking at the first row)
• Creating histograms to visualize distributions
• Producing scatter plots to asses the relationships between variables
• Conduct dimensional reductions to simplify the dataset
• Cluster the data to identify groups and patterns

This usually brings dashboards, graphs, and other visual representations of data.

The objective of this phase is not to understand every minor detail, but to get a good sense of the trends to make a proper model.

To visualize data, we use tools like Power BI, Tableau and Python libraries like matplotlib.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is an iterative process combining analysis and visualization to understand data.