✅ Must-Know Data Science Concepts for Interviews 📊💼

📍 Statistics & Probability
1. Descriptive vs Inferential statistics
2. Probability distributions (Normal, Binomial, Poisson)
3. Hypothesis testing & p-values
4. Central Limit Theorem
5. Confidence intervals

📍 Data Wrangling & Cleaning
6. Handling missing data
7. Data imputation methods
8. Outlier detection
9. Data transformation & normalization
10. Feature scaling

📍 Machine Learning Basics
11. Supervised vs Unsupervised learning
12. Common algorithms: Linear Regression, Logistic Regression, Decision Trees
13. Overfitting vs Underfitting
14. Bias-Variance tradeoff
15. Evaluation metrics (accuracy, precision, recall, F1-score)

📍 Advanced Machine Learning
16. Random Forests & Gradient Boosting
17. Support Vector Machines
18. Neural Networks basics
19. Dimensionality reduction (PCA, t-SNE)
20. Cross-validation techniques

📍 Python & Libraries
21. NumPy basics (arrays, broadcasting)
22. Pandas (dataframes, indexing)
23. Matplotlib & Seaborn (visualization)
24. Scikit-learn (model building & metrics)
25. Handling large datasets

📍 Data Visualization
26. Types of charts (bar, line, histogram, scatter)
27. Choosing the right visualization
28. Dashboard basics
29. Plotly & interactive viz
30. Storytelling with data

📍 Big Data & Tools
31. Hadoop basics
32. Spark fundamentals
33. SQL queries for data extraction
34. Data warehousing concepts
35. Cloud services (AWS, GCP, Azure)

📍 Deep Learning
36. CNN & RNN overview
37. Backpropagation
38. Transfer learning
39. Frameworks (TensorFlow, PyTorch)
40. Model tuning & optimization

📍 Business & Communication
41. Translating business problems to data tasks
42. KPIs and metrics understanding
43. Presenting insights effectively
44. Storytelling with data
45. Ethics & privacy considerations

📍 Tools & Workflow
46. Git & version control
47. Jupyter notebooks & reproducibility
48. Docker basics
49. Experiment tracking
50. Collaboration in teams

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