Data Science MCQ Questions And Answers

The following quiz “Data Science MCQ Questions And Answers” provides Multiple Choice Questions (MCQs) related to Data Science. These Data Science MCQs are also Interviews (campus interview, walk-in interview, company interview), Placement or recruitment, entrance examinations, and competitive examinations oriented. You can practice the below questions to improve your Data Science skills. You can click on the View Answer button to check the answer. Let’s solve this Data Science MCQ Questions And Answers Quiz. In simple words, Data science is the field of applying advanced analytics techniques, processes, algorithms and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses.

You can practice these Data Science MCQs here and if you want a pdf of Data Science MCQ Questions And Answers, we will provide a downloading link here soon so please keep visiting here for further modifications.

1. Data science is the process of diverse set of data through ?

  1. Organizing data
  2. Processing data
  3. Analysing data
  4. All of the above

Answer : D
Explanation: Data science is the field which includes organizing data, processing data and analysing data to extract valuable information from data for business decision-making, strategic planning, etc. So, All of the above is correct.

2. Point out the correct statement.

  1. Raw data is original source of data
  2. Preprocessed data is original source of data
  3. Raw data is the data obtained after processing steps
  4. None of the above

Answer : A
Explanation: Raw data is original source of data is the correct answer. So, option A is correct.

3. How do we perform Bayesian classification when some features are missing?

  1. We integrate the posteriors probabilities over the missing features
  2. We ignore the missing features
  3. We assuming the missing values as the mean of all values
  4. Drop the features completely

Answer : A
Explanation: When some features are missing, while performing Bayesian classification we don’t use general methods of handling missing values but we integrate the posteriors probabilities over the missing features for better predictions. So, option A is correct.

4. The modern conception of data science as an independent discipline is sometimes attributed to?

  1. John McCarthy
  2. Arthur Samuel
  3. William S.
  4. Dennis Ritchie

Answer : C
Explanation: William S. developed data science.

Machine learning MCQ questions and answers

5. ________ graph displays information as a series of data points connected by straight line segments.

  1. Bar
  2. Scatter
  3. Histogram
  4. Line

Answer : D
Explanation: A line graph displays information as a series of data points connected by straight line segments.

6. Data fishing is sometimes referred to as

  1. Data bagging
  2. Data dredging
  3. Data merging
  4. None of the mentioned

Answer : B
Explanation: Data fishing is sometimes referred to as Data dredging so option B is correct.

7. Which is one of the significant data science skills?

  1. Statistics
  2. Data Visualization
  3. Machine Learning
  4. All of the above

Answer : D
Explanation: All of the above is the significant data science skills.

8. A method used to make vector of repeated values?

  1. read()
  2. data()
  3. rep()
  4. view()

Answer : B
Explanation: data() method used to make vector of repeated values.

9. Which of the following step is performed by the data scientist after acquiring the data?

  1. Data Replication
  2. Data Integration
  3. Data Cleansing
  4. All of the Mentioned

Answer : C
Explanation: Data cleansing, data cleaning or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a database, table, or record set and it is generally performed by data scientist after acquiring the data.

10. K- nearest neighbors algorithm is based on ______ learning

  1. Supervised
  2. Unsupervised
  3. Association
  4. Correlation

Answer : B
Explanation: K- nearest neighbors algorithm is based on unsupervised learning.

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