Top 30+ Data Science Interview Questions & Answers [2022]

In recent years, the field of data science has grown in popularity. Data scientists are experts who can use coding and algorithms to simplify big data and transform it into a business problem-solving solution.

The following are some of the most commonly asked questions in Data Scientist job interviews, both for freshers and for seasoned Data Scientists.

Data Science Interview Questions

1. What is Data Science?

Well, Data Science is basically a set of algorithms, tools, and machine learning techniques that helps in the discovery of hidden patterns from a sample of raw data.

2. What do you understand by logistic regression in Data Science?

It’s a technique for predicting a binary outcome using a linear combination of predictor variables. Another name for logical regression is the “logit model”.

3. Can you name the three types of biases that can occur during sampling?

The three types of biases that can occur during sampling process are:

Selection bias;
Under coverage bias; and
Survivorship bias.

4. What is A/B testing, and how does it work?

A/B testing compares two variants of a single variable, the control, and variant, using hypothesis testing and two-sample hypothesis testing. It’s often used to enhance and optimize the user experience and marketing.

5. What is a Decision Tree Algorithm?

A decision tree is basically a common supervised machine learning algorithm. It’s also used for Classification and Regression.

It helps you to break down a large dataset into smaller subsets. Both categorical and numerical data can be handled by the decision tree.

6. What exactly are tensors?

Tensors are mathematical structures that describe a set of higher-dimensional data inputs such as alphabets, numerals, and rank that are fed into a neural network.

7. What are Recommender Systems and How Do They Work?

It’s a subclass of information filtering methods. It aids in predicting the preferences or ratings that users are likely to impart on a product.

8. What is the concept of systematic sampling?

A statistical technique in which elements are chosen from an ordered sampling frame is known as systematic sampling. The list is progressed in a circular fashion in systematic sampling because once you hit the end of the list, it is started over from the beginning. The equal probability method is the best example of systematic sampling.

9. What is the difference between prior probability and likelihood?

The likelihood is the probability of classifying a given observant in the presence of another variable, while the prior probability is the proportion of the dependent variable in the data set.

10. In the Decision Tree, what is pruning?

Pruning is a machine learning and search algorithm technique for reducing the size of decision trees by removing parts of the decision tree that have little power to classify instances. Pruning, or the reverse of splitting, is the method of removing sub-nodes from a decision node.

11. What are three drawbacks to using a linear model?

The following are the three drawbacks to using a linear model:

The linearity of the errors assumption.
It is incapable of resolving a variety of overfitting problems.
This model cannot be used to predict binary or count outcomes.

12. What is Ensemble Learning, and how does it work?

Ensemble Learning is the process of integrating a diverse group of learners (individual models) to improve the model’s stability and predictive power.

13. How Much Should an Algorithm Be Updated?

When you want to update an algorithm, do the following:

As data flows across infrastructure, you want the model to evolve.
The underlying data source is in the process of changing.
There is a non-stationarity scenario.
The algorithm works poorly, and the results are inaccurate.

14. Why is resampling done?

Resampling is done on below cases:

Drawing randomly with substitution from a collection of data points or using as subsets of available data to estimate the precision of sample statistics.
When conducting necessary tests, substituting labels on data points.
Random subsets are used to validate models.

15. What is a recall?

The true positive rate divided by the actual positive rate yields a recall. It has a scale of 0 to 1.

16. What is precision?

The most widely used error metric is precision, in a classification mechanism. It has a scale of 0 to 1, with 1 representing 100%.

17. When it comes to recall and precision, what’s the difference?

The fraction of instances that have been classified as true is known as recall. Precision, on the other hand, is a measure for weighing instances that are actually true.

Precision is a true value that represents factual knowledge, while recall is an approximation.

18. Make a list of Python libraries for data analysis and scientific computations.

The list of Python libraries are:

Pandas
Matplotlib
NumPy
SciKit
Seaborn
SciPy

19. What is the concept of bias?

Bias is an error introduced into the model as a result of a machine learning algorithm’s oversimplification.” It can lead to underfitting.

20. What is the concept of power analysis?

The power analysis is an essential component of any experimental design. It assists you in determining the sample size needed to determine the impact of a given size from a cause with a certain degree of confidence. It also allows you to deploy a specific probability in a sample size constraint.

21. What are the objectives of A/B testing?

Random experiments with two variables, A and B, were conducted using AB testing. The aim of this testing method is to determine what improvements should be made to a web page in order to improve or increase a strategy’s outcome.

22. What do you understand by Deep Learning?

Machine learning has a subtype called deep learning. It’s about artificial neural networks and the algorithms that are inspired by them.

23. What is the difference between Eigenvalue and Eigenvector?

Understanding linear transformations require the use of eigenvectors. For a covariance matrix or correlation, data scientists must calculate the eigenvectors. Eigenvalues are the directions along which a linear transformation compresses, flips, or stretches the data.

24. What are your thoughts on Artificial Neural Networks?

Artificial Neural Networks (ANNs) are a type of machine learning algorithm that has revolutionized the field. It enables you to adjust to changing input. As an outcome, the network produces the best possible result without having to change the output criteria.

25. What is collaborative filtering and how does it work?

Collaborative filtering is a method of searching for correct patterns using multiple data sources, multiple agents, and collaborating viewpoints.

26. What is a Linear Regression, and how does it work?

A statistical programming method in which the score of a variable ‘A’ is predicted from the score of a second variable ‘B’ is known as linear regression. B is referred to as the predictor variable, and A is referred to as the criterion variable.

27. What is a Random Forest, and how does it work?

Random forest is a machine learning method that can be used to perform various regression and classification tasks. It’s also used to deal with missing data and outlier values.

28. What does the term cross-validation mean?

Cross-validation is a validation technique for determining how statistical analysis results can generalize for independent datasets. This method is used in situations where the goal is to forecast and it is necessary to estimate how accurate a model would be.

29. What is Back Propagation and How Does It Work?

The core of neural net training is back-propagation. It is a method of tuning a neural net’s weights based on the error rate obtained in the previous epoch.

30. What is Normal Distribution and How Does It Work?

A collection of continuous variables distributed over a normal curve or in the form of a bell curve is known as a normal distribution. It can be thought of as a continuous probability distribution with statistical applications. By using the standard distribution curve, it is beneficial to analyze the variables and their relationships.

31. What is the concept of a univariate analysis?

Univariate analysis is a form of analysis that applies to none attribute at a time. The Boxplot is a popular univariate model.

So these are the best data science interview questions and answers to get a job.

Know more about Data science – Wikipedia