Statistics: Probability distributions Hypothesis testing Regression analysis Descriptive statistics Machine Learning: Basics of supervised and unsupervised learning Model evaluation metrics (accuracy, precision, recall, F1 score) Overfitting and underfitting Feature engineering Basic SQL: CRUD operations (Create, Read, Update, Delete) Joins and subqueries Grouping and aggregation Indexing for performance Round 2: Statistics: Advanced probability concepts Time series analysis Bayesian statistics A/B testing Machine Learning: Deep dive into specific algorithms (e.g., decision trees, SVM, neural networks) Ensemble methods Hyperparameter tuning Cross-validation techniques Basic SQL: Complex queries involving multiple tables Window functions Performance optimization strategies Data normalization and denormalization