This course provides a comprehensive guide to Exploratory Data Analysis (EDA) techniques. Learn how to systematically investigate datasets to discover patterns, spot anomalies, test hypotheses, and check assumptions using statistical summaries and graphical representations. Acquire practical skills in data cleaning, transformation, and visualization using common Python libraries like Pandas, Matplotlib, and Seaborn.
Prerequisites Basic Python programming knowledge
Level:
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.