1. What is Data Science?  

   Data Science is an interdisciplinary field that uses statistical, mathematical, and computational techniques to extract insights and knowledge from structured and unstructured data. It involves data collection, cleaning, exploration, modeling, and interpretation.  

2. What are the steps involved in a data science project?  

   • Problem Definition  

   • Data Collection  

   • Data Cleaning and Preparation  

   • Exploratory Data Analysis (EDA)  

   • Feature Engineering  

   • Model Building  

   • Model Evaluation  

   • Model Deployment  

   • Monitoring and Maintenance  

3. What is the difference between supervised and unsupervised learning?  

   • Supervised Learning: Involves training a model on labeled data (input-output pairs).  

   • Unsupervised Learning: Involves training a model on data without labeled outcomes, focusing on identifying patterns and structures.  

4. What is overfitting, and how can it be prevented?  

   Overfitting occurs when a model learns the noise in the training data instead of the underlying pattern, leading to poor generalization on new data. It can be prevented using techniques like cross-validation, pruning, regularization (L1/L2), and reducing model complexity.  

5. Explain the bias-variance tradeoff.  

   • Bias: Error due to overly simplistic assumptions in the model. High bias leads to underfitting.  

   • Variance: Error due to too much complexity in the model. High variance leads to overfitting.  

   The tradeoff is balancing bias and variance to minimize overall error.  

6. What is cross-validation?  

   Cross-validation is a technique for evaluating a model’s performance by dividing the data into multiple subsets and training/testing the model on these subsets. The most common type is k-fold cross-validation, where the data is split into k parts, and the model is trained on k-1 parts while the remaining part is used for validation.  

7. What is the difference between precision and recall?  

   • Precision: The ratio of true positives to the total predicted positives (True Positives / (True Positives + False Positives)).  

   • Recall (Sensitivity): The ratio of true positives to the total actual positives (True Positives / (True Positives + False Negatives)).  

8. What is the F1 Score?  

   The F1 Score is the harmonic mean of precision and recall, providing a single metric that balances both. It’s calculated as:  

   F1 = 2 × (Precision × Recall) / (Precision + Recall)  

9. What is regularization?  

   Regularization involves adding a penalty to the model's loss function to prevent overfitting by discouraging complex models. The most common types are L1 (Lasso) and L2 (Ridge) regularization.  

10. What is a confusion matrix?  

    A confusion matrix is a table used to evaluate the performance of a classification model, showing the true positives, true negatives, false positives, and false negatives.  

11. Explain the difference between Type I and Type II errors.  

    • Type I Error (False Positive): Rejecting a true null hypothesis (finding an effect when there isn’t one).  

    • Type II Error (False Negative): Failing to reject a false null hypothesis (missing an effect that is there).  

12. What is a ROC curve?  

    A Receiver Operating Characteristic (ROC) curve is a graph showing the performance of a classification model at different threshold settings, plotting the True Positive Rate (TPR) against the False Positive Rate (FPR).  

13. What is the AUC-ROC?  

    The Area Under the ROC Curve (AUC-ROC) is a single scalar value that summarizes the performance of a classifier, where a value closer to 1 indicates a better-performing model.  

14. What are ensemble methods?  

    Ensemble methods involve combining multiple models (weak learners) to improve overall performance. Common techniques include Bagging (e.g., Random Forest) and Boosting (e.g., XGBoost, AdaBoost).  

15. What is gradient boosting?  

    Gradient Boosting is an ensemble technique that builds models sequentially, with each model trying to correct the errors of the previous ones, typically used in decision trees.  

16. Explain the concept of feature selection.  

    Feature selection is the process of selecting the most important features in a dataset to improve model performance and reduce overfitting. Techniques include Recursive Feature Elimination (RFE), Lasso Regression, and using feature importance from models like Random Forest.  

17. What is PCA (Principal Component Analysis)?  

    PCA is a dimensionality reduction technique that transforms the data into a new set of uncorrelated variables called principal components, ordered by the amount of variance they capture.  

18. What is the difference between correlation and covariance?  

    • Covariance: Measures how two variables change together. It is sensitive to the scale of the variables.  

    • Correlation: A normalized version of covariance that measures the strength and direction of a linear relationship between two variables.  

19. What is a time series?  

    A time series is a sequence of data points collected or recorded at specific time intervals. Time series analysis involves understanding and forecasting data points in the sequence.  

20. What is ARIMA?  

    ARIMA (AutoRegressive Integrated Moving Average) is a statistical model used for time series forecasting, combining aspects of autoregression (AR), differencing (I), and moving average (MA).  

21. What is the difference between a Data Scientist and a Data Analyst?  

    • Data Analyst: Focuses on interpreting and analyzing data to provide actionable insights, often using descriptive statistics and visualizations.  

    • Data Scientist: Works on advanced analytics, including building models, creating algorithms, and performing predictive analysis.  

22. What is deep learning?  

    Deep Learning is a subset of machine learning involving neural networks with many layers (deep networks). These models are capable of learning complex patterns in data, especially in tasks like image recognition and natural language processing.  

23. What is a neural network?  

    A neural network is a computational model inspired by the human brain, consisting of layers of interconnected nodes (neurons). Each connection has a weight, and the network learns by adjusting these weights based on the error in its predictions.  

24. What is dropout in neural networks?  

    Dropout is a regularization technique used in neural networks where randomly selected neurons are ignored during training, reducing overfitting by preventing the network from relying too heavily on particular neurons.  

25. What is a hyperparameter?  

    Hyperparameters are settings or configurations external to the model that are set before training (e.g., learning rate, number of layers in a neural network). They differ from model parameters, which are learned during training.  

26. What is a Random Forest?  

    A Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.  

27. What is a p-value?  

    A p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis.  

28. What is a hypothesis test?  

    A hypothesis test is a statistical method used to make decisions about the population based on sample data. It involves comparing a null hypothesis against an alternative hypothesis using test statistics and p-values.  

29. What is A/B testing?  

    A/B testing is a method of comparing two versions of a webpage or product to determine which one performs better. It involves randomly assigning users to one of the two versions and analyzing the results.  

30. What is a cohort analysis?  

    Cohort analysis is a technique used to study the behavior of groups (cohorts) of users over time. It allows businesses to understand trends and patterns in the data related to a specific group.