Exploratory Data Analysis (EDA)
Before fitting any model, you need to understand your data. Exploratory Data Analysis (EDA) is the structured process of examining a dataset to understand its distributions, relationships, and anomalies — and to surface the questions that should guide further analysis.
This module equips you with the full EDA toolkit: numerical summaries, distribution plots, correlation analysis, and outlier detection. These are the skills that distinguish analysts who find real insight from those who jump straight to modeling on poorly understood data.
Lessons
- Introduction to EDA — What EDA is, the questions it answers, and a systematic checklist to apply to any new dataset.
- Summary Statistics — Mean, median, standard deviation, percentiles, and the
.describe()method for rapid tabular summaries. - Histograms — Distribution shapes (normal, skewed, bimodal), bin selection, and interpreting frequency distributions.
- Box Plots — Quartiles, the interquartile range, whiskers, and using box plots to compare distributions across groups.
- Correlation Matrix — Pearson correlation, heatmaps, and identifying which features are most related to a target variable.
- Outliers — Defining outliers with the IQR method and z-scores, visualizing them, and deciding when to remove versus investigate.
- Module Assessment — An EDA assessment requiring you to analyze a new dataset, produce visualizations, and write interpretive conclusions.