Data wrangling and cleaning form the foundation of any advanced data analysis. Even the most sophisticated algorithms will fail if the underlying data is inaccurate, inconsistent, or incomplete. This module focuses on techniques that help analysts transform raw, messy data into high-quality, structured datasets suitable for analysis and modeling.

Exploratory Data Analysis (EDA) is one of the most important stages in the data science workflow. It helps analysts understand data patterns, detect anomalies, and discover relationships between variables before building models.

Advanced statistical methods help analysts move beyond simple averages and correlations to truly understand why patterns exist in data. These methods test ideas, validate models, and quantify uncertainty forming the foundation of scientific and data-driven reasoning.