✅ Data Science Interview Cheat Sheet (2025 Edition)

✅ 1. Data Science Fundamentals
• What is Data Science?
• Data Science vs Data Analytics vs ML
• Lifecycle: Problem → Data → Insights → Action
• Real-World Applications: Fraud detection, Personalization, Forecasting

✅ 2. Data Handling & Analysis
• Data Collection & Cleaning
• Exploratory Data Analysis (EDA)
• Outlier Detection, Missing Value Treatment
• Feature Engineering
• Data Normalization & Scaling

✅ 3. Statistics & Probability
• Descriptive Stats: Mean, Median, Variance, Std Dev
• Inferential Stats: Hypothesis Testing, p-value
• Probability Distributions: Normal, Binomial, Poisson
• Confidence Intervals, Central Limit Theorem
• Correlation vs Causation

✅ 4. Machine Learning Basics
• Supervised & Unsupervised Learning
• Regression (Linear, Logistic)
• Classification (SVM, Decision Tree, KNN)
• Clustering (K-Means, Hierarchical)
• Model Evaluation: Confusion Matrix, AUC, F1 Score

✅ 5. Data Visualization
• Python Libraries: Matplotlib, Seaborn, Plotly
• Dashboards: Power BI, Tableau
• Charts: Line, Bar, Heatmaps, Boxplots
• Best Practices: Clear titles, labels, color usage

✅ 6. Tools & Languages
• Python: Pandas, NumPy, Scikit-learn
• SQL for querying data
• Jupyter Notebooks
• Git & Version Control
• Cloud Platforms: AWS, GCP, Azure basics

✅ 7. Business Understanding
• Defining KPIs & Metrics
• Telling Stories with Data
• Communicating insights clearly
• Understanding Stakeholder Needs

✅ 8. Bonus Concepts
• Time Series Analysis
• A/B Testing
• Recommendation Systems
• Big Data Basics (Hadoop, Spark)
• Data Ethics & Privacy

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