Top 50 Data Science Interview Questions and Answers

 

Top 50 Data Science Interview Questions and Answers

If you are looking to make it big in the tech sector, then you must be looking towards data science too. Since is it a leading field in the world of technology today, it comes as no surprise that those who know the industry too well are also of the opinion that they should be moving ahead with this technology too.

With machine learning and big data on the rise, data scientists are one of the most in-demand professionals around the world.

But how can you ensure super success in your career? It is by acing your interview! This blog will help you pass your interview in your dream company by giving you a glimpse into the top 50 data science interview questions and answers.

 

Question 1. Tell us what is data science in a single sentence.

Answer. Data science is the coming together of machine learning techniques, tools and algorithms to help find hidden patterns from a given set of raw data.

Question 2. How many biases are known to occur during sampling?

Answer. Three major biases are known to occur during sampling, namely under coverage bias, selection bias and survivorship bias.

Question 3. What are Recommender Systems?

Answer. Recommender Systems are subclasses of information filtering techniques that aid in the prediction of ratings or preferences by users to a specific product.

Question 4. Explain Power Analysis.

Answer. An important part of the experimental design, Power Analysis aids in determining the size of the sample required to extract the effect of a selected size from a cause, along with a certain level of assurance.

Question 5. Explain bias.

Answer. Bias is essentially an error that arises in a model due to the oversimplication of a certain machine learning algorithm and often leads to underfitting.

Question 6. What do you understand by Ensemble Learning?

Answer. It is a method wherein two varied ser of learners are combined for the purpose of improvising on the predictive power and the stability of the model.

Question 7. How many types of Ensemble Learning methods are there?

Answer. There are two types of Ensemble Learning methods namely Bagging and Boosting.

Question 8. Explain ANN.

Answer. ANN is the abbreviation for Artificial Neutral Networks and are a special set of algorithms which have played a huge role in revolutionizing machine learning. As the input keeps changing, it keeps helping you adopt accordingly, ensuring that the best result is generated by the network without having to redesign the output criteria.

Question 9. Can you explain Random Forest to us?

Answer. A machine learning method, Random Forest aids in the performance of all types of classification and regression tasks. Outlier and missing values can also be treated with it.

Question 10. How will you explain logistic regression in data science to us?

Answer. Also known as the logit model, logistic regression is a method employed to predict the binary outcome from a linear combination of predicator variables.

Question 11. Which machine learning algorithm is usually used for classification and regression?

Answer. The Decision Tree is a well-known supervised machine learning algorithm that is majorly used for classification and regression.

Question 12. Can you give us two disadvantages of employing a linear model?

Answer. The two disadvantages of employing a linear model are –

• It cannot be used for count or binary outcomes
• Many overfitting problems cannot be solved

Question 13. Name a few libraries in Python being used for Scientific Computations and Data Analysis.

Answer. A few libraries include –

• Seaborn
• Pandas
• SciKit
• SciPy
• Matplotli

Question 14. Give us two reasons behind performing resampling.

Answer. Resampling needs to be done for the below reasons –

• Using random subsets to validate models
• Substituting labels on data points while performing important tests

Question 15. What is Collaborative filtering?

Answer. Collaborative filtering is employed to search for the right patterns by collaborating various agents, data sources and viewpoints.

Question 16. What is the Naive Bayes Algorithm model based upon?

Answer. The said model is based upon the Bayes Theorem.

Question 17. Explain Linear Regression in a single line.

Answer. It is a statistical programming method wherein the score of a variable ‘A’ is used as a basis to predict the score of variable ‘B’.

Question 18. A/B testing is conducted with what aim?

Answer. A/B testing is done to bring out the points that can be implemented or changed in a web page to increase or enhance the outcome of the strategy employed.

Question 19. Is deep learning a subset of machine learning?

Answer. Yes, deep learning is a subset of machine learning and is concerned with algorithms that take inspiration by the ANN structure.

Question 20. Explain Normal Distribution.

Answer. It is a set of a continuous variable that is spread across in the shape of a bell curve or a normal curve. It can be considered as a continuous probability distribution.

Question 21. Is R better for text analytics or Python?

Answer. Python is definitely more suited for text analytics for the simple reason that it contains a rich library called pandas that allows the usage of high-level data structure and data analysis tools.

Question 22. Why should statistics be used by data scientists?

Answer. Statistics give a data scientist better and clearer idea about the customer’s expectation. By employing statistics, we can garner knowledge related to consumer behaviour, retention, engagement and interest, amongst others.

Question 23. Give us any 5 types of Deep Learning Frameworks.

Answer. Five types of deep learning frameworks are –

• Caffe
• Pytorch
• TenserFlow
• Keras
• Chainer

 

Question 24. What do you understand by Boltzmann Machine?

 

Answer. It is a simple learning algorithm that aids in the discovery of features representing complicated regularities in the training data. Weights and quantity for any given problem can be optimized with this algorithm.

Question 25. Can you explain Auto-Encoder in simple terms?

Answer. In simplest of terms, Auto-Encoders are learning networks that enables the transformation of outputs into inputs with lesser numbers of errors.

Question 26. Why is it essential to maintain clean data and practice data cleansing?

Answer. Data cleansing is essential as data dirty can lead to a faulty inside that can in turn damage the prospect of the organization.

Question 27. What is uniform distribution?

Answer. When the data that is spread is equally in the range, it is said to be uniform distribution.

Question 28. What is the opposite of uniform distribution?

Answer. Skewed distribution is the opposite of uniform distribution.

Question 29. When does underfitting happen in a static model?

Answer. When a machine learning algorithm or a statistical model is unable to pick up the underlying trend of a given set of data, underfitting occurs.

Question 30. What are some of the most commonly used algorithms?

Answer. Some of the most commonly used algorithms include –

• KNN
• Logistic regression
• Linear regression
• Random forest

Question 31. What should the end result of reinforcement learning be?

Answer. The end result of reinforcement learning should be to help the organization in increasing the binary reward signal.

Question 32. What is the reinforcement learning method based on?

Answer. The reinforcement learning method is based upon the penalty/ reward mechanism.

Question 33. Explain univariate analysis.

Answer. Univariate analysis is one which is not applied to any attribute at a time.

Question 34. What is the range of precision?

Answer. The range of precision is from 0 to 1, wherein 1 represents 100%.

Question 35. Explain precision.

Answer. The most widely employed error metric is precision.

Question 36. What is a Test Set?

Answer. Test set is used to evaluate or test the performance and calibre of a trained Machine learning model.

Question 37. What is a Validation Set?

Answer. Considered to be a part of the training set, a validation set is employed for the parameter selection that aids in deviating from overfitting of the model that is being build.

Question 38. What is a recall?

Answer. It is a ratio of the true positive rate upon the actual positive rate.

Question 39. What is the recall range?

Answer. The recall range is from 0 to 1.

Question 40. Can the correlation between the categorical variable and the continuous variable be captured?

Answer. Yes, the correlation between the categorical variable and the continuous variable can be captured.

Question 41. How can the between the categorical variable and the continuous variable be captured?

Answer. To capture the correlation between the categorical variable and the continuous variable, we need to use analysis of covariance technique.

Question 42. Based upon the usage of the statistics, what are the major categories of sampling techniques?

Answer. Based upon the usage of the statistics, there are two major categories of sampling techniques –

• Probability sampling techniques
• Non-probability sampling techniques

Question 43. What is imbalanced data?

Answer. In case the data is distributed highly unequally across multiple categories, it is said to be imbalanced. The end result of imbalanced data is errors in model performance along with inaccuracy in results.

Question 44. Define DOE.

Answer. Abbreviation for design of experience, DOE represents the task design that aims to explain information and describe the variation under hypothesized conditions reflecting variables.

Question 45. Define KPI.

Answer. Key performance indicator or KPI helps in measuring how well a business is achieving its objectives.

Question 46. How many types of selection bias are there?

Answer. There are four main types of selection bias named –

• Time interval
• Attrition
• Sampling bias
• Data

Question 47. What does MSE and RMSE stand for in linear regression model?

Answer. MSE stands for Mean Squared Error and RMSE stands for Root Mean Square Error in linear regression model.

Question 48. What are the two main components in a GAN?

Answer. GAN stands for Generative Adversarial Network and its two main components are Discriminator and Generator.

Question 49. What are the most commonly used techniques for cross-validation?

Answer. The most commonly used techniques for cross-validation are –

• Leave p-out method
• Holdout method
• Leave one-out method
• K- Fold method

Question 50. What are the advantages of data cleaning?

Answer. Data cleaning is beneficial because –

• Increased data quantity
• Increased accuracy & efficiency
• Maintains data consistency
• Complete data
• Error free data