Data wrangling MCQ

What is data wrangling and why is it important?

• Data wrangling is the same as data visualization.

• Data wrangling is necessary and sufficient for large datasets.

• Data wrangling is the process of cleaning and organizing raw data for analysis

• Data wrangling is the process of collecting data from various sources.

List common techniques used in data wrangling.

• Data analysis

• Data visualization

• Data cleaning, data transformation, data merging

• Data storage

How can merging datasets improve data analysis?

• Merging datasets provides more complete and integrated view of the information.

• Merging datasets decreases the accuracy of the results.

• Merging datasets limits the scope of analysis to only one dataset.

• Merging datasets complicates data analysis by creating redundancy.

What is the difference between reshaping and combining datasets?

• Reshaping combines datasets; combining changes their structure.

• Reshaping changes the structure of data; combining merges multiple datasets.

• Reshaping sorts data; combining filters data.

• Reshaping duplicates data; combining removes duplicates.

Why is it essential to handle large volumes of data effectively?

• To simplify data entry processes

• To increase data redundancy

• To reduce storage costs

• To ensure accurate analysis, improve decision-making.

Describe a scenario where data wrangling is necessary.

• Data wrangling is required to increase the speed of internet connections.

• Data wrangling is necessary for creating new software applications.

• Data wrangling is necessary to clean and standardize customer feedback collected from multiple sources.

• Data wrangling is used to enhance the visual appeal of a website.

How does data wrangling impact the quality of insights derived from data?

• Data wrangling improves data quality, leading to more accurate and actionable insights.

• Data wrangling decreases data accuracy, leading to misleading insights.

• Data wrangling complicates data analysis, resulting in less reliable insights.

• Data wrangling has no effect on the quality of insights derived from data.

What are some common challenges faced during data wrangling?

• Data inconsistency and missing values

• Data visualization and reporting

• Data entry and collection

• Data storage and retrieval

How does data wrangling contribute to machine learning projects?

• It prepares and cleans data, ensuring better model performance.

• It complicates the machine learning process.

• It is irrelevant to machine learning.

• It replaces the need for data analysis.

What role does data cleaning play in the data analysis process?

• Data cleaning is optional and can be skipped.

• Data cleaning ensures that the data is accurate and reliable for analysis.

• Data cleaning only focuses on removing duplicates.

• Data cleaning is the same as data visualization.