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.

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?

complicated JSON from facebook API

complicated JSON from Twitter API

unformatted excel file

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q12.

Point out the correct statement.

Nearly 80% of data analysis is spent on wrangling data

Nearly 20% of data analysis is spent on data dredging

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

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?

each variable in one column

each observation in different row

one table for each kind of variable

none of the mentioned

Discuss

Answer: (b).each observation in different row

Q14.

Which of the following package is used for tidy data?

tidyr

souryr

NumPy

all of the mentioned

Discuss

Answer: (a).tidyr

Q15.

Point out the wrong statement.

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

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

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

None of the mentioned

Discuss

Answer: (d).None of the mentioned

Q16.

Which of the following process involves structuring datasets to facilitate analysis?

Data tidying

Data mining

Data booting

All of the mentioned

Discuss

Answer: (a).Data tidying

Q17.

Strange binary file generated from machines is an example of tidy data.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False

Q18.

Which of the following is the most common problem with messy data?

Column headers are values

Variables are stored in both rows and columns

A single observational unit is stored in multiple tables

All of the mentioned

Discuss

Answer: (d).All of the mentioned

Q19.

tidyr is a reframing of _______ designed to accompany the tidy data framework.

reshape5

dplyr

reshape2

all of the mentioned

Discuss

Answer: (c).reshape2

Q20.

Raw data in the real-world is tidy and properly formatted.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True

Page 2 of 6

Welcome to the Data Manipulation MCQs Page

Data Manipulation MCQs | Page 2 of 6

Explore more Topics under Data Science

Data Science Basics

Data Analysis with Python

Data Analysis and Research

Statistical Inference and Regression Models

Machine Learning

Developing Data Products

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