Data Wrangling

Assume that you are working on a data science project and needs to analyze dataset. Do you think data wrangling important in your analysis? Why.

• Yes. Data wrangling is primarily focused on data visualization.

• Yes. Data wrangling is a step that can be skipped in analysis.

• Yes. Data wrangling prepares and cleans data for analysis.

• No. Data wrangling is only necessary for large datasets.

What are the common steps involved in data wrangling process. Choose the one in correct order.

• data collection,

  data cleaning,

  data transformation, data enrichment, data validation

• data collection,

  Data Analysis

  data cleaning,

  data transformation

• data collection,

  data transformation, Data visualization

• Data storage

  Data Analysis

  Data Visualization

Krish is working on a project that involves analyzing customer feedback data. Help him to understand the difference between data cleaning and data wrangling?

• Data cleaning means correcting errors. Data wrangling means transforming data into a usable format.

• Data cleaning is the same as data wrangling.

• Data wrangling only involves data visualization.

• Data cleaning is only about removing duplicates. Data Wrangling means Data analysis

What is data normalization?

• Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity.

• Data normalization is the process of deleting all data from a database.

• Data normalization involves creating multiple copies of the same data for backup.

• Data normalization is the method of encrypting data to enhance security.

How to handle missing data during the wrangling process?

• Assume missing data is zero

• Duplicate existing data to fill gaps

• Ignore missing data completely

• Remove, impute, or encode missing data.

What is the significance of data visualization in data wrangling?

• Data visualization helps in identifying patterns and anomalies during data wrangling

• Data visualization has no impact on decision-making.

• Data visualization complicates the data wrangling process.

• Data visualization is only useful for creating reports.

What is the importance of data enrichment in the data wrangling process?

• Data enrichment is only necessary for small datasets.

• Data enrichment adds additional relevant information to the dataset, improving the quality

• Data enrichment is the process of visualizing data.

• Data enrichment involves deleting unnecessary data.

What is the importance of data validation in the data wrangling process?

• Data validation ensures that the data is accurate, complete, and meets the required standards before analysis.

• Data validation is the same as data cleaning.

• Data validation is not necessary if the dataset is large.

• Data validation only applies to numerical data.

How can outliers affect the data wrangling process?

• Outliers are only relevant in large datasets.

• Outliers can skew the results of data analysis and may need to be addressed during wrangling.

• Outliers have no impact on data wrangling.

• Outliers should always be removed from the dataset.

What techniques can be used for data imputation in the wrangling process?

• Data imputation only applies to categorical data.

• Mean, median, or mode substitution are common techniques for data imputation.

• Data imputation involves deleting all missing values.

• Data imputation is not necessary if the dataset is small.