Post #2408

✅ Top 50 Data Analytics Interview Questions – Part 2📊🔥 1️⃣1️⃣ Explain different types of data: structured, semi-structured, unstructured. ⦁ Structured: Organized in rows and columns (e.g., SQL tables). ⦁ Semi-structured: Some structure, but not in tabular form (e.g., JSON, XML). ⦁ Unstructured: No predefined structure (e.g., images, videos, text files). 1️⃣2️⃣ What is Data Normalization? Data normalization reduces data redundancy and improves integrity by organizing fields and tables. It typically involves breaking large tables into smaller ones and defining relationships. 1️⃣3️⃣ Explain EDA (Exploratory Data Analysis). EDA is used to understand the structure and patterns in data using: ⦁ Descriptive stats (mean, median) ⦁ Visualizations (histograms, boxplots) ⦁ Correlation analysis It helps to form hypotheses and detect anomalies. 1️⃣4️⃣ What is the difference between Supervised and Unsupervised Learning? ⦁ Supervised: Labeled data used (e.g., regression, classification). ⦁ Unsupervised: No labels; find patterns (e.g., clustering, PCA). 1️⃣5️⃣ What is Overfitting and Underfitting? ⦁ Overfitting: Model performs well on training but poorly on test data. ⦁ Underfitting: Model fails to capture patterns in training data. 1️⃣6️⃣ What are Confusion Matrix and its metrics? A matrix showing predicted vs actual results: ⦁ TP, TN, FP, FN Metrics: Accuracy, Precision, Recall, F1-Score 1️⃣7️⃣ Difference between Regression and Classification? ⦁ Regression: Predicts continuous values (e.g., price). ⦁ Classification: Predicts categories (e.g., spam/ham). 1️⃣8️⃣ What is Feature Engineering? Process of creating new features or transforming existing ones to improve model performance. 1️⃣9️⃣ What is A/B Testing? A/B Testing compares two versions (A & B) to see which performs better using statistical analysis. 2️⃣0️⃣ Explain ROC and AUC. ⦁ ROC Curve: Plots TPR vs FPR. ⦁ AUC: Area under ROC, measures model’s ability to distinguish between classes. 💬Tap ❤️ for Part 3!