Data Science
Interview Questions

The data science process typically includes problem definition, data collection, data cleaning and preprocessing, exploratory data analysis, feature engineering, model building, model evaluation, and deployment. It is an iterative process where each step informs and refines the subsequent ones.

In supervised learning, the algorithm is trained on labeled data, while unsupervised learning involves working with unlabeled data to discover patterns and relationships.

Feature engineering involves creating new features or modifying existing ones to improve a model's performance. It aims to provide more relevant and informative input data for machine learning algorithms.

EDA is crucial for understanding the characteristics of the data, identifying patterns, outliers, and relationships between variables. It helps in making informed decisions about data preprocessing, feature selection, and model development.

Handling missing data involves techniques like imputation (replacing missing values with estimated ones), removing rows or columns with missing values, or using advanced methods like predictive modeling to fill in missing values.

Variance measures the spread of a single random variable, while covariance measures the degree to which two variables change together. Covariance can be positive, negative, or zero, indicating the nature and strength of the relationship between variables.

Regularization is a technique used to prevent overfitting by adding a penalty term to the model's cost function. It discourages complex models and helps improve the model's ability to generalize to new, unseen data.

Cross-validation is a technique used to assess a model's performance by splitting the dataset into multiple subsets, training the model on different subsets, and evaluating its performance on the remaining data. It helps in obtaining a more reliable estimate of the model's generalization performance.

Use this question as an opportunity to showcase a specific project from your experience, covering aspects like problem definition, data exploration, feature engineering, model development, and the impact of your work on the business or project goals.

Supervised learning involves training a model on labeled data (with known outcomes), such as predicting housing prices. Unsupervised learning works with unlabeled data, like clustering similar documents without predefined categories.

Outliers can significantly impact statistical analyses and machine learning models. Handling outliers involves techniques such as transformation, truncation, or using robust statistical measures. It's crucial because outliers can skew results and affect model performance.

A/B testing involves comparing two versions (A and B) of a webpage, feature, or product to determine which performs better. It is widely used in data science to assess the impact of changes and make data-driven decisions for optimization.

Feature scaling standardizes the range of independent variables. It benefits machine learning models by preventing attributes with larger scales from dominating the learning process, ensuring fair contributions from all features.

Recommendation systems predict a user's preference for items, such as movies, based on historical behavior or similar users' preferences. Applications include personalized content recommendations on streaming platforms or product recommendations in e-commerce.

Handling missing data involves evaluating the impact on the analysis, exploring imputation methods, and clearly documenting any assumptions made. If data is crucial and cannot be imputed, I would consider alternative sources or assess the feasibility of proceeding with the available data.