Top 51 Data Science Interview Basic Questions in 2024

Data Science Interview Basic Question

This article discusses the 51 data science interview basic questions. These questions are very relevant to the interview and are among the commonly asked questions for data science interview questions. Therefore, Candidates can perform well in the interview round after knowing the answers to these questions. Those 51 interview basic questions are provided below

What are the Interview Basic Questions

1. What is Data Science?

2. Differentiate between Data Analytics and Data Science.

3. How is Python Useful?

4. How R is Useful in the Data Science Domain?

5. What is Supervised Learning?

6. What is Unsupervised Learning?

7. What do you understand about near Regression?

8. What do you understand by logistic regression?

9. What is a confusion matrix?

10. What do you understand about the true-positive rate and false-positive rate?

11. How is Data Science different from traditional application programming?

12. Explain the differences between supervised and unsupervised learning.

13. What is the difference between long-format data and wide-format data?

14. Mention some techniques used for sampling. What is the main advantage of sampling?

15. What is bias in data science?

16. What is dimensionality reduction?

16. What is dimensionality reduction?

17. Why is Python used for data cleaning in DS?

18. Why is R used in Data Visualization?

19. What are the popular libraries used in Data Science?

20. What are important functions used in Data Science?

21. What is k-fold cross-validation?

22. Explain how a recommender system works.

23. What is Poisson Distribution?

24. What is a normal distribution?

25. What is Deep Learning?

26. What is CNN (Convolutional Neural Network)?

27. What is an RNN (recurrent neural network)?

28. Explain selection bias.

29. Between Python and R, which one will you choose for analyzing the text, and why?

30. Explain the purpose of data cleaning

31. What do you understand from Recommender System? and State its application

32. What is Gradient Descent?

33. What are the various skills required to become Data Scientist?

34. What is TensorFlow?

35. What is Dropout?

36. State any five Deep Learning Frameworks.

37. Define Neural Networks and its types

38. What is the ROC curve?

39. What do you understand by a decision tree?

40. What do you understand by a random forest model?

41. Two candidates, Aman and Mohan appear for a Data Science Job interview. The probability of Aman cracking the interview is 1/8 and that of Mohan is 5/12. What is the probability that at least one of them will crack the interview?

42. How is Data modeling different from Database design?

43. What is precision?

44. What is a recall?

45. What is the F1 score and how to calculate it?

46. What is a p-value?

47. Why do we use p-value?

48. What is the difference between an error and a residual error?

49. Why do we use the summary function?

50. How are Data Science and Machine Learning related to each other?

51. Explain univariate, bivariate, and multivariate analyses.

10 Data Science Interview Basic Question And Answer

1. What is Data Science?
   Ans. Data Science is an interdisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, and computer science to analyze and interpret complex data sets, often involving big data.

2. Differentiate between Data Analytics and Data Science

Ans.Data Analytics: Focuses on processing and performing statistical analysis of existing datasets to uncover trends, patterns, and insights that can guide decision-making.

Data Science: Encompasses a broader scope, including not only data analysis but also machine learning, predictive modeling, and the development of algorithms.

3. How is Python useful?

Ans. Python is a widely used programming language in the field of Data Science. Its popularity is attributed to its readability, versatility, and vast ecosystem of libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.

4. How R is Useful in the Data Science Domain?

Ans.R is another programming language commonly used in Data Science. It is particularly strong in statistical computing and data visualization. R provides a wide range of packages for statistical analysis and has a dedicated community of statisticians and data scientists. While Python is versatile, R is often favored for its statistical capabilities.

5. What is Supervised Learning?

Ans. Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, which means the input data is paired with the corresponding correct output. The algorithm learns from this labeled data to make predictions or decisions when given new, unseen data.

6.What is Unsupervised Learning?

Ans.Unsupervised learning involves training a model on unlabeled data. The algorithm tries to identify patterns, relationships, or structures within the data without explicit guidance on the output. Clustering and dimensionality reduction are common tasks in unsupervised learning.

7. What do you understand about near Regression?

Ans.Linear Regression is a statistical method used in machine learning to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. The goal is to find the best-fit line that minimizes the difference between predicted and actual values.

8. What do you understand by logistic regression?

Ans.Logistic Regression is a statistical method used for binary classification problems. Unlike linear regression, it models the probability that a given input belongs to a particular category. The output is transformed using the logistic function, providing a probability score between 0 and 1.

9. What is a confusion matrix?

Ans.A confusion matrix is a table used to evaluate the performance of a classification algorithm. It compares the actual values of a dataset with the predicted values and categorizes them into four groups: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN).

10. What do you understand about the true-positive rate and false-positive rate?

Ans.True-Positive Rate (Sensitivity or Recall): It is the ratio of correctly predicted positive observations to the total actual positives. It is a measure of how well a model can identify positive instances.

False-Positive Rate: It is the ratio of incorrectly predicted positive observations to the total actual negatives. It indicates the proportion of negative instances that were incorrectly classified as positive.

Conclusion

This article summarizes the 51 basic interview questions for the data science interview. Through learning these questions, each candidate’s skill and knowledge will be improved.