10+ Data Science Interview Questions and Answers

List and tuple are both data structures in Python used to store collections of items.

• Lists are mutable, meaning their values can be changed after creation.

• Tuples are immutable, meaning their values cannot be changed after creation.

• Lists are defined using square brackets [], while tuples are defined using parentheses ().

• Lists are typically used for collections of similar items, while tuples are used for collections of different items.

• Example of a list: my_list = [1, 2, 3]

• Examp...read more

Data structures are ways of organizing and storing data in a computer so that it can be accessed and used efficiently.

• Data structures can be linear or non-linear

• Examples of linear data structures include arrays, linked lists, and stacks

• Examples of non-linear data structures include trees and graphs

• Choosing the right data structure is important for optimizing performance

Regression is a statistical method used to establish a relationship between a dependent variable and one or more independent variables.

• Regression helps to predict the value of the dependent variable based on the values of the independent variables.

• It involves fitting a line or curve to the data points to minimize the difference between the predicted and actual values.

• There are different types of regression such as linear regression, logistic regression, polynomial regression,...read more

HAVING is used with GROUP BY to filter groups, WHERE is used to filter rows

• HAVING is used with GROUP BY to filter groups based on aggregate functions

• WHERE is used to filter rows based on conditions

• HAVING is applied after GROUP BY, WHERE is applied before GROUP BY

• Example: SELECT department, AVG(salary) FROM employees GROUP BY department HAVING AVG(salary) > 50000;

• Example: SELECT * FROM employees WHERE age > 30;

SQL query to retrieve bank transactions data.

• Use SELECT statement to retrieve data from transaction table.

• Filter data based on account number or transaction date.

• Group data by transaction type or amount for analysis.

Different metrics for evaluating regression models

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• Mean Absolute Error (MAE)

• R-squared (Coefficient of Determination)

Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

• The theorem states that regardless of the shape of the population distribution, the sampling distribution of the sample mean will be approximately normally distributed.

• It is a fundamental concept in statistics and is used in hypothesis testing and confidence intervals.

• For example, if you take multiple samples of a population and calculat...read more

A machine learning model is a mathematical model that learns from data to make predictions or decisions without being explicitly programmed.

• ML models can be classified into categories such as supervised learning, unsupervised learning, and reinforcement learning.

• Examples of ML models include linear regression, decision trees, support vector machines, and neural networks.

• ML models require training data to learn patterns and relationships, and testing data to evaluate their per...read more

Data preprocessing is the process of cleaning, transforming, and organizing raw data before analysis.

• Removing irrelevant or duplicate data

• Handling missing values

• Normalizing or standardizing data

• Encoding categorical variables

• Feature scaling

• Data transformation (e.g. log transformation)

• Data reduction (e.g. PCA)

• Handling outliers

A cluster is a group of data points or objects that are similar to each other within the group and dissimilar to data points in other groups.

• Clusters are formed based on the similarity of data points within the group.

• Clustering is an unsupervised learning technique used in data science.

• Examples of clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

Developed a machine learning model to predict customer churn for a telecommunications company.

• Collected and cleaned customer data including demographics, usage patterns, and customer service interactions.

• Used classification algorithms such as logistic regression and random forest to build the predictive model.

• Evaluated model performance using metrics like accuracy, precision, recall, and ROC curve.

• Implemented the model into the company's CRM system to identify at-risk custome...read more