Prerequisites: Basic Python programming (including Pandas and NumPy) and foundational statistics knowledge assumed.

Level: Intermediate

What You'll Learn

• EDA Fundamentals

  Understand the objectives and systematic process of Exploratory Data Analysis.

• Data Loading & Inspection

  Load various data formats and perform initial inspections to understand structure and types.

• Data Cleaning Techniques

  Identify and handle missing values, duplicates, and inconsistencies in datasets.

• Univariate Analysis

  Analyze single variables using statistical summaries and visualizations like histograms and box plots.

• Bivariate Analysis

  Investigate relationships between pairs of variables using scatter plots, correlation analysis, and cross-tabulations.

• Multivariate Visualization

  Employ techniques like pair plots and heatmaps to visualize relationships among multiple variables.

• Data Transformation

  Apply basic transformations like scaling and encoding for analytical purposes.

• Reporting Findings

  Structure and communicate findings derived from the EDA process effectively.