Data Science Interview Notes (Full-Stack Roadmap)

How to use this site

• 🟪 1-minute Summary = skim mode
• 🟦 Core Notes = must-know
• 🟨 Interview Triggers = what interviewers really test
• 🟥 Common Mistakes = traps
• 🟩 Mini Example = quick application

0) Start Here (Read First)

• How to study this blog for interviews
• Reusable lesson template
• Cheat sheets index

A) Statistics Foundations

A1: Statistics Basics

• Descriptive vs Inferential Statistics
• Mean / Median / Mode
• Range / IQR / Variance / Standard Deviation

A2: Probability

• Probability Fundamentals
• Types of Probability

A3: Random Variables & Distributions

• Random Variables (Discrete vs Continuous)
• Distributions Overview
• Normal Distribution
• Standard Normal Distribution (Z-score)

B) Statistical Inference & Hypothesis Testing

B1: Hypothesis Testing Core (Master Template)

• Hypothesis Testing: General Step-by-Step Framework

B2: Test Families (Each page repeats the same framework)

• Chi-Square Tests
• t-test / z-test
• F-test
• ANOVA
• Standard Error (supporting concept)

C) EDA & Data Preparation

C1: EDA Workflow

• EDA: General Steps (Master Checklist)
• Draw General Conclusions from Data

C2: Data Cleaning Modules

• Null / Missing Value Treatment
• Duplicate Treatment
• Outlier Treatment

D) Machine Learning Core

D1: Unsupervised Learning (Clustering)

• Clustering Overview
• Dendrogram / Hierarchical Clustering
• K-Means Clustering

D2: Supervised Learning (Prediction)

• Supervised Learning Overview
• Linear Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Tree

E) Model Evaluation & Model Selection

E1: Regression Evaluation

• Regression Metrics Overview
• RMSE
• MAPE
• Linear Regression Score (R² etc.)

E2: Classification Evaluation

• Confusion Matrix
• Classification Report (Precision/Recall/F1)
• Accuracy
• Precision
• Recall
• FPR (False Positive Rate)
• ROC Curve + AUC

E3: Model Selection Workflows

• Cross Validation (CV) Overview
• Grid Search CV

F) Generalization, Regularization, and Fit

F1: Fit & Generalization

• Overfitting
• Underfitting

F2: Regularization (Linear Models)

• What is Regularization?
• Ridge Regression
• Lasso Regression

G) Feature Engineering & Non-Linear Modeling

• Non-Linear Modeling Overview
• Polynomial Features

H) Imbalanced Data Toolkit

• Imbalanced Data Overview
• Undersampling

I) Ensemble Methods

I1: Ensemble Overview

• Ensemble Methods Overview

I2: Bagging & Forests

• Random Forest

I3: Boosting Family

• AdaBoost
• Gradient Boosting
• XGBoost

Suggested Reading Paths

Path 1 — Interview Sprint (fast catch-up)

1. A1 → A3 (Stats + distributions)
2. B1 (Hypothesis testing steps)
3. C1 + C2 (EDA workflow + cleaning)
4. D2 (Linear + Logistic + Trees)
5. E2 (Confusion matrix + ROC/AUC)
6. F1 + F2 (Over/Under + regularization)
7. E3 (CV + GridSearch)
8. I (Ensembles)

Path 2 — Deep Study (build mastery)

Follow pillars A → I in order and do the mini example + interview questions on every page.