🔹 DATA SCIENCE – INTERVIEW REVISION SHEET

1️⃣ What is Data Science?
> “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.”

Difference from Data Analytics:
• Data Analytics → past  present (what/why)
• Data Science → future  automation (what will happen)

2️⃣ Data Science Lifecycle (Very Important)
1. Business problem understanding
2. Data collection
3. Data cleaning  preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature engineering
6. Model building
7. Model evaluation
8. Deployment  monitoring
Interview line:
> “I always start from business understanding, not the model.”

3️⃣ Data Types
• Structured → tables, SQL
• Semi-structured → JSON, logs
• Unstructured → text, images

4️⃣ Statistics You MUST Know
• Central tendency: Mean, Median (use when outliers exist)
• Spread: Variance, Standard deviation
• Correlation ≠ causation
• Normal distribution
• Skewness (income → right skewed)

5️⃣ Data Cleaning  Preprocessing
Steps you should say in interviews:
1. Handle missing values
2. Remove duplicates
3. Treat outliers
4. Encode categorical variables
5. Scale numerical data
Scaling:
• Min-Max → bounded range
• Standardization → normal distribution

6️⃣ Feature Engineering (Interview Favorite)
> “Feature engineering is creating meaningful input variables that improve model performance.”
Examples:
• Extract month from date
• Create customer lifetime value
• Binning age groups

7️⃣ Machine Learning Basics
• Supervised learning: Regression, Classification
• Unsupervised learning: Clustering, Dimensionality reduction

8️⃣ Common Algorithms (Know WHEN to use)
• Regression: Linear regression → continuous output
• Classification: Logistic regression, Decision tree, Random forest, SVM
• Unsupervised: K-Means → segmentation, PCA → dimensionality reduction

9️⃣ Overfitting vs Underfitting
• Overfitting → model memorizes training data
• Underfitting → model too simple
Fixes:
• Regularization
• More data
• Cross-validation

🔟 Model Evaluation Metrics
• Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC
• Regression: MAE, RMSE
Interview line:
> “Metric selection depends on business problem.”

1️⃣1️⃣ Imbalanced Data Techniques
• Class weighting
• Oversampling / undersampling
• SMOTE
• Metric preference: Precision, Recall, F1, ROC-AUC

1️⃣2️⃣ Python for Data Science
Core libraries:
• NumPy
• Pandas
• Matplotlib / Seaborn
• Scikit-learn
Must know:
• loc vs iloc
• Groupby
• Vectorization

1️⃣3️⃣ Model Deployment (Basic Understanding)
• Batch prediction
• Real-time prediction
• Model monitoring
• Model drift
Interview line:
> “Models must be monitored because data changes over time.”

1️⃣4️⃣ Explain Your Project (Template)
> “The goal was . I cleaned the data using . I performed EDA to identify . I built model and evaluated using . The final outcome was .”

1️⃣5️⃣ HR-Style Data Science Answers
Why data science?
> “I enjoy solving complex problems using data and building models that automate decisions.”
Biggest challenge:
“Handling messy real-world data.”
Strength:
“Strong foundation in statistics and ML.”

🔥 LAST-DAY INTERVIEW TIPS
• Explain intuition, not math
• Don’t jump to algorithms immediately
• Always connect model → business value
• Say assumptions clearly

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