Data Exploration and Cleaning

Visualize univariate distributions using histograms, box plots, and density plots to identify skewness, spread, and modality.

Visualize relationships between variables using scatter plots, pair plots, and correlation heatmaps.

Identify data quality issues such as missing values, outliers, and class imbalance through exploratory analysis.

Identify common patterns of missing data and determine appropriate handling strategies based on data context.

Handle missing data using deletion and simple imputation strategies like mean, median, and mode.

Detect outliers using statistical methods such as the IQR and Z-score.

Handle outliers through removal, capping/winsorization, and log transformation.

Identify and resolve duplicate records and common data inconsistencies in a dataset.