25 Data Science MCQs with Answers

Amidst the data deluge, career-minded individuals are drawn to the field of Data Science, recognizing its vast potential. From data enthusiasts to seasoned professionals, the opportunities in this domain are just exploding.

As evidenced by the projected 35% employment growth until 2032, data science continues to shape industries and economies worldwide.

Encompassing key concepts that are simultaneously challenging and require an active mindset, the field seeks talented and skilled professionals.

Data Scientist enthusiasts who have just finished learning can target variousData Science job roles.

However, self-assessment is also important to land a desired job.

‍After completing a data science course, engaging in questions and answers helps reinforce understanding, clarify concepts, and apply knowledge to real-world scenarios, fostering deeper learning and retention.

Explore our curated collection of Data Science multiple-choice questions (MCQs) designed to evaluate your proficiency in statistics and Python.

Data Science MCQs with Answers

Q1. Which among these is based on feedback-based Machine Learning?

1. Supervise Machine Learning
2. Unsupervised Machine Learning
3. Semi-supervised Machine Learning
4. Reinforcement Machine Learning

Answer: 4. Reinforcement Machine Learning

Q2. In how many ways can you analyze data in Data Science?

1. 2
2. 3
3. 4
4. 7

Answer: 3. 4

Q3.  Which data analysis is concerned with steps and actions to be taken in the future to obtain a specific outcome?

1. Predictive data analysis
2. Descriptive data analysis
3. Prescriptive data analysis
4. Diagnostic data analysis

Answer: 3. Prescriptive data analysis

 Q4. Which of these functions is not suitable for importing csv files in R?

1. read.csv()
2. read_excel()
3. read.table()
4. Both a and b

Answer: 2. read_excel()

Q5. In which library will you find class() in R programming language?

1. class
2. stats
3. base
4. utils

Answer: 3. base

Q6. In Python, what output can you expect with time.time()

1. Current time in milliseconds only
2. Past 1 hour time
3. Current time in milliseconds since midnight of January 1, 1970, GMT
4. Current time in seconds since midnight of January 1, 1970, GMT

Answer: 3. Current time in milliseconds since midnight of January 1, 1970 GMT

Q7. Identify among the options which is not a core data type

1. Class
2. Dictionary
3. Lists
4. Tuples

Answer: 1. Class

Q8. Which type of statistics uses probability and is suitable to generalize a large data set?

1. Descriptive statistics
2. Inferential statistics
3. Both a and b
4. Statistics is not used for this task

Answer: 2. Inferential statistics

Q9. Which of these actions identifies data properties?

1. Data wrangling
2. Data mining
3. Data cleaning
4. Both a and b

Answer: 2. Data mining

Q10. Why do you sample data for data analysis?

1. To increase the dataset size
2. To decrease the dataset size
3. To decrease dimensionality
4. To select a representative subset of data

Answer: 4. To select a representative subset of data

Q11. Which supervised learning algorithm is preferable for data classification?

1. Random Forest
2. k-Means
3. Principal Component Analysis
4. Hierarchical Clustering

Answer: 1. Random Forest

Q12. Which method will you use to reduce the impact of outliers on the dataset?

1. Data transformation
2. Data cleaning
3. Robust scaling
4. Data processing

Answer: 3. Robust scaling

Q13. Select the commonly used algorithm in data science regression

1. Naive Bayes
2. k-Means
3. Logistic Regression
4. Decision Tree

Answer: 3. Logistic Regression

Q14. Under which rule does Procedural Domain Knowledge fit when considering a rule-based system?

1. Condition-Action Rule
2. Production Rule
3. Meta Rule
4. Control Rule
5. Both a and b

Answer: 5. Both a and b

Q15. What do you understand by K in the k-Mean algorithm?

1. Number of iterations
2. Number of attributes
3. Number of clusters
4. Number of data

Answer: 4. Number of clusters

Q16. What is the purpose of data munging?

1. Evaluation of model performance
2. Data visualizations
3. Preparing data for analysis
4. Feature selection

Answer: 3. Preparing data for analysis

Q17. What is the critical factor in choosing an appropriate node during tree construction?

1. Attribute with the highest information gain
2. Attribute with the high information gain and entropy
3. Attribute with the lowest information gain
4. Attribute with the high entropy

Answer: 1. Attribute with the highest information gain

Q18. What will be the consequence of the wrong choice of learning rate value in gradient descent?

1. Slow convergence
2. Local minima
3. Oscillations
4. All of the above

Answer: 4. All of the above

Q19. Which is the correct operation to fix violations in the Red-Black Tree after node deletion?

1. Balancing
2. Trimming
3. Recoloring
4. None of the above

Answer: 3. Recoloring

Q20. Choose the Python data structure responsible for the storage and manipulation of tabular data in Data Science.

1. Array
2. List
3. Dictionary
4. DataFrame

Answer: 4. DataFrame

Q21. Which type of ML algorithm is a Decision Tree?

1. Supervised Machine Learning
2. Unsupervised Machine Learning
3. Semi-supervised Machine Learning
4. Reinforcement Machine Learning

Answer: 1. Supervised Machine Learning

Q22.  Linear regression models are preferable for

1. Interpretation
2. Predictions
3. Conclusion
4. Both a and b

Answer: 2. Predictions

Q23. Which of these is not the Meta Character of Regex in data analytics?

1. *
2. #
3. {}
4. ^

Answer: 2. #

Q24.  Which is preferable for text analysis among Python and R?

1. Python, quick storing
2. R, quick sorting
3. Python, high-performance data
4. R, high-performance data

1. 1 and 3
2. 1 and 4
3. 2 and 4
4. 2 and 3

Answer: a. 1 and 3

Q25. What do you understand by ‘Naive’ in Naive Bayes?

1. Independence of variables in the dataset
2. Based on Bayes theorem
3. Dependent dataset
4. Both a and b

Answer: 1. Independence of variables in the dataset

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