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Data Science MCQs with answers Page - 5

Dear candidates you will find MCQ questions of Data Science here. Learn these questions and prepare yourself for coming examinations and interviews. You can check the right answer of any question by clicking on any option or by clicking view answer button.
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Mr. Dubey • 51.17K Points
Coach

Q. Which of the following command line environment is used for interacting with Git?

(A) GitHub
(B) Compound linear regression is not equipped to handle more than one predictor
(C) Git Boot
(D) All of the mentioned

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Mr. Dubey • 51.17K Points
Coach

Q. Point out the wrong statement.

(A) Simple linear regression is equipped to handle more than one predictor
(B) git command -d
(C) Linear regression consists of finding the best-fitting straight line through the points
(D) All of the mentioned

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Mr. Dubey • 51.17K Points
Coach

Q. What is the term for the process of removing or reducing noise and inconsistencies from data?

(A) Data Integration
(B) Data Transformation
(C) Data Aggregation
(D) Data Cleansing

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Mr. Dubey • 51.17K Points
Coach

Q. Which of the following best describes the purpose of data sampling in Data Science?

(A) To analyze the entire dataset
(B) To select a representative subset
(C) To visualize data
(D) To calculate data statistics

M

Mr. Dubey • 51.17K Points
Coach

Q. Which statistical measure represents the spread or dispersion of data values in a dataset?

(A) Median
(B) Mean
(C) Standard Deviation
(D) Mode

M

Mr. Dubey • 51.17K Points
Coach

Q. In Data Science, what is the term for a data point that is missing a value for one or more features?

(A) Outlier
(B) Anomaly
(C) Null Value
(D) Feature

M

Mr. Dubey • 51.17K Points
Coach

Q. What is the primary objective of data exploration in Data Science?

(A) To build predictive models
(B) To find hidden patterns
(C) To summarize data
(D) To collect data

M

Mr. Dubey • 51.17K Points
Coach

Q. Which type of data is represented by categories or labels and cannot be measured numerically?

(A) Numerical Data
(B) Categorical Data
(C) Continuous Data
(D) Ordinal Data

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