Data Analysis Cheat Sheet

Data analysis is the systematic process of inspecting, cleaning, transforming, and modeling data to discover useful information and support decision-making. It encompasses exploratory data analysis (EDA), data cleaning, wrangling, and feature engineering—the critical preparatory steps that transform raw data into analysis-ready datasets. While often associated with statistics and machine learning, data analysis fundamentally serves as the bridge between raw observations and actionable insights. A key insight: spending adequate time on quality data preparation typically has a larger impact on model performance than algorithm selection itself—well-prepared data enables simpler models to outperform complex ones trained on poor data. Modern workflows additionally use SHAP-based feature importance, automated drift detection, and schema validation tools to ensure pipelines remain robust and production-ready.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 152 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Exploratory Data Analysis (EDA) TechniquesTable 2: Data Cleaning TechniquesTable 3: Outlier Detection and TreatmentTable 4: Data Transformation and ScalingTable 5: Feature Encoding for Categorical VariablesTable 6: Feature Engineering TechniquesTable 7: Dimensionality Reduction and Feature SelectionTable 8: Data Aggregation and GroupingTable 9: Data Reshaping and MergingTable 10: Statistical Analysis and Hypothesis TestingTable 11: Data Wrangling OperationsTable 12: Data Quality and ValidationTable 13: Data Visualization for AnalysisTable 14: Sampling and Balancing TechniquesTable 15: Automated EDA and Monitoring Tools

Table 1: Exploratory Data Analysis (EDA) Techniques

Technique	Example	Description
summary statistics	`df.describe()` `df.info()`	• Computes descriptive measures—count, mean, std, min, quartiles, max for numerical columns • `info()` shows data types and null counts.
value counts	`df['category'].value_counts(normalize=True)`	• Counts frequency of unique values in categorical columns • `normalize=True` returns proportions instead of counts.
univariate analysis	`df['age'].hist(bins=30)` `df['category'].value_counts()`	• Examines single variable distributions using histograms, box plots, and frequency counts • reveals central tendency, spread, and outliers for one feature at a time.
bivariate analysis	`df.plot.scatter(x='age', y='income')` `df.groupby('region')['sales'].mean()`	• Explores relationships between two variables through scatter plots, grouped aggregations, and cross-tabulations • identifies correlations and patterns.
multivariate analysis	`sns.pairplot(df, hue='target')` `df.corr()`	• Examines interactions among three or more variables using pair plots, correlation matrices, and heatmaps • reveals complex dependencies.
correlation analysis	`df.corr()` `sns.heatmap(df.corr(), annot=True)`	• Calculates pairwise correlation coefficients between numerical features • heatmap visualization reveals multicollinearity and feature relationships.

Data Analysis Cheat Sheet

Table 1: Exploratory Data Analysis (EDA) Techniques

Technique	Example	Description
summary statistics	`df.describe()` `df.info()`	• Computes descriptive measures—count, mean, std, min, quartiles, max for numerical columns • `info()` shows data types and null counts.
value counts	`df['category'].value_counts(normalize=True)`	• Counts frequency of unique values in categorical columns • `normalize=True` returns proportions instead of counts.
univariate analysis	`df['age'].hist(bins=30)` `df['category'].value_counts()`	• Examines single variable distributions using histograms, box plots, and frequency counts • reveals central tendency, spread, and outliers for one feature at a time.
bivariate analysis	`df.plot.scatter(x='age', y='income')` `df.groupby('region')['sales'].mean()`	• Explores relationships between two variables through scatter plots, grouped aggregations, and cross-tabulations • identifies correlations and patterns.
multivariate analysis	`sns.pairplot(df, hue='target')` `df.corr()`	• Examines interactions among three or more variables using pair plots, correlation matrices, and heatmaps • reveals complex dependencies.
correlation analysis	`df.corr()` `sns.heatmap(df.corr(), annot=True)`	• Calculates pairwise correlation coefficients between numerical features • heatmap visualization reveals multicollinearity and feature relationships.