Data Scientist interview questions

A data scientist's role in a company is to analyze and interpret complex data to assist in decision-making, identify trends, and solve business problems using data-driven insights.

CRISP-DM stands for Cross-Industry Standard Process for Data Mining. It is a six-phase approach (Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment) used to provide a structured process for data mining projects.

Data scientists commonly use programming languages like Python, R, SQL, and sometimes Java, Julia, or Scala for data analysis and machine learning projects.

To handle missing data, I would first try to understand the nature of the missing data. Then, I might choose to omit or fill in missing data using techniques such as mean/mode substitution, interpolation, or more sophisticated methods like data imputation using models.

Supervised learning involves training a model on a labeled dataset, which means the model learns from inputs as well as the correct outputs. Unsupervised learning, on the other hand, involves training a model on data without labeled responses, and the model tries to identify patterns or groupings on its own.

Overfitting occurs when a model learns the training data too well, capturing noise along with the underlying data pattern, performing well on training data but poorly on unseen data. It can be prevented using techniques such as cross-validation, pruning, regularization, and using simpler models.

A confusion matrix is a table used to evaluate the performance of a classification model. It helps in understanding the model's accuracy, precision, recall, and other important metrics by showing the number of true positives, true negatives, false positives, and false negatives.

In hypothesis testing, a p-value is a measure of the probability that an observed difference could have occurred just by random chance. A lower p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, suggesting the results are statistically significant.

Feature selection can be done using methods such as decision trees, LASSO regularization, information gain, and correlation coefficients. The goal is to reduce the number of input variables to enhance computation efficiency and model performance.

When balancing stakeholders' interests, it's important to communicate effectively, understand each stakeholder's needs and constraints, and use data to provide insights that align with both business goals and ethical considerations. This might involve designing a solution that optimizes resources while ensuring fairness, privacy, and transparency.