Data Science Core Cheat Sheet

Updated 2026-04-21

Next Topic: Data Validation and Quality in Data Science Cheat Sheet

Data Science is the interdisciplinary field combining statistics, mathematics, and programming to extract insights from data and drive evidence-based decision-making. It spans the full analytical lifecycle—from collecting and cleaning raw data to building predictive models, validating results, and deploying solutions that solve real-world business and scientific problems. Understanding the foundational workflow is essential: data rarely arrives clean or analysis-ready, and a single misstep in preprocessing or evaluation can invalidate an otherwise sophisticated model. As of 2026, the field increasingly emphasizes reproducible pipelines, model monitoring in production, and causal reasoning alongside traditional predictive modeling.

What This Cheat Sheet Covers

This topic spans 30 focused tables and 243 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Data Science Lifecycle StagesTable 2: Data Collection MethodsTable 3: Data Cleaning TechniquesTable 4: Missing Data Imputation MethodsTable 5: Exploratory Data Analysis (EDA) TechniquesTable 6: Feature Engineering MethodsTable 7: Categorical Encoding TechniquesTable 8: Data Transformation and ScalingTable 9: Sampling TechniquesTable 10: Outlier Detection MethodsTable 11: Dimensionality Reduction TechniquesTable 12: Feature Selection MethodsTable 13: Handling Imbalanced DataTable 14: Cross-Validation TechniquesTable 15: Hyperparameter Tuning MethodsTable 16: Model Evaluation Metrics (Classification)Table 17: Model Evaluation Metrics (Regression)Table 18: Regularization TechniquesTable 19: Ensemble Learning MethodsTable 20: Bias-Variance Tradeoff ConceptsTable 21: Statistical Hypothesis TestingTable 22: P-Value and Significance ConceptsTable 23: Probability DistributionsTable 24: Experimental Design TechniquesTable 25: Correlation vs Causation ConceptsTable 26: Data Leakage PreventionTable 27: Time Series Analysis ComponentsTable 28: Data Drift and MonitoringTable 29: Data Quality AssessmentTable 30: Model Interpretability and Explainability

Table 1: Data Science Lifecycle Stages

Stage	Example	Description
Problem Definition	`Define business question:` `"Predict customer churn"`	• Clarifies the business goal and translates it into a measurable analytical objective • guides all subsequent work.
Data Collection	`df = pd.read_csv('data.csv')`	• Gathers data from databases, APIs, files, or sensors • the quality and breadth of collected data directly impacts model performance.
Data Preparation	`df.dropna(inplace=True)`	• Cleans, transforms, and structures raw data into analysis-ready format • typically consumes 60–80% of project time.
Exploratory Data Analysis (EDA)	`df.describe()` `df.hist()`	Visualizes and summarizes data to identify patterns, outliers, and relationships before modeling.
Feature Engineering	`df['ratio'] = df['A'] / df['B']`	Creates new variables or transforms existing ones to improve model predictive power.

Table 1: Data Science Lifecycle Stages

Stage	Example	Description
Problem Definition	`Define business question:` `"Predict customer churn"`	• Clarifies the business goal and translates it into a measurable analytical objective • guides all subsequent work.
Data Collection	`df = pd.read_csv('data.csv')`	• Gathers data from databases, APIs, files, or sensors • the quality and breadth of collected data directly impacts model performance.
Data Preparation	`df.dropna(inplace=True)`	• Cleans, transforms, and structures raw data into analysis-ready format • typically consumes 60–80% of project time.
Exploratory Data Analysis (EDA)	`df.describe()` `df.hist()`	Visualizes and summarizes data to identify patterns, outliers, and relationships before modeling.
Feature Engineering	`df['ratio'] = df['A'] / df['B']`	Creates new variables or transforms existing ones to improve model predictive power.