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Welcome to the Data Manipulation MCQs Page

Dive deep into the fascinating world of Data Manipulation with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Data Manipulation, a crucial aspect of Data Science. In this section, you will encounter a diverse range of MCQs that cover various aspects of Data Manipulation, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Science.

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Check out the MCQs below to embark on an enriching journey through Data Manipulation. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Science.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Data Manipulation. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Data Manipulation MCQs | Page 2 of 6

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Q11.
Which of the following is an example of tidy data?

a.

complicated JSON from facebook API

b.

complicated JSON from Twitter API

c.

unformatted excel file

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q12.
Point out the correct statement.

a.

Nearly 80% of data analysis is spent on wrangling data

b.

Nearly 20% of data analysis is spent on data dredging

c.

Nearly 80% of data analysis is spent on the cleaning and preparing data

d.

None of the mentioned

Discuss
Answer: (c).Nearly 80% of data analysis is spent on the cleaning and preparing data
Q13.
Which of the following is a trait of tidy data?

a.

each variable in one column

b.

each observation in different row

c.

one table for each kind of variable

d.

none of the mentioned

Discuss
Answer: (b).each observation in different row
Q14.
Which of the following package is used for tidy data?

a.

tidyr

b.

souryr

c.

NumPy

d.

all of the mentioned

Discuss
Answer: (a).tidyr
Q15.
Point out the wrong statement.

a.

Tidy datasets are all alike but every messy dataset is messy in its own way

b.

Most statistical datasets are data frames made up of rows and columns

c.

Tidy datasets provide a standardized way to link the structure of a dataset with its semantics

d.

None of the mentioned

Discuss
Answer: (d).None of the mentioned
Q16.
Which of the following process involves structuring datasets to facilitate analysis?

a.

Data tidying

b.

Data mining

c.

Data booting

d.

All of the mentioned

Discuss
Answer: (a).Data tidying
Q17.
Strange binary file generated from machines is an example of tidy data.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (b).False
Q18.
Which of the following is the most common problem with messy data?

a.

Column headers are values

b.

Variables are stored in both rows and columns

c.

A single observational unit is stored in multiple tables

d.

All of the mentioned

Discuss
Answer: (d).All of the mentioned
Q19.
tidyr is a reframing of _______ designed to accompany the tidy data framework.

a.

reshape5

b.

dplyr

c.

reshape2

d.

all of the mentioned

Discuss
Answer: (c).reshape2
Q20.
Raw data in the real-world is tidy and properly formatted.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (a).True
Page 2 of 6

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