AP CSP Data Cleaning

What is data cleaning?

• Data cleaning is the process of encrypting data for security purposes

• Data cleaning is the process of deleting all data to start fresh

• Data cleaning is the process of identifying and correcting errors or inconsistencies in data to improve its quality.

• Data cleaning is the process of creating new data from scratch

Why is data cleaning important in AP CSP?

• Data cleaning is important in AP CSP to ensure accurate, complete, and reliable data for analysis.

• Data cleaning only applies to certain types of data in AP CSP

• Data cleaning can be done after analysis in AP CSP

• Data cleaning is unnecessary in AP CSP

What are some common techniques used in data cleaning?

• Ignoring missing values completely

• Adding more noise to the data

• Mixing up data formats further

• Removing duplicates, handling missing values, correcting inconsistent data formats, standardizing data values, detecting outliers

Explain the concept of missing data in data cleaning.

• Missing data in data cleaning refers to the presence of values in a dataset

• Missing data in data cleaning refers to the absence of values in a dataset, which can occur due to various reasons such as human error, equipment malfunction, or data corruption.

• Missing data in data cleaning is always due to software bugs

• Missing data in data cleaning is intentional and does not affect the analysis

How can outliers be handled during data cleaning?

• Remove outliers if they are due to errors or extreme values, or transform them using techniques like winsorization or log transformation.

• Create a separate dataset for outliers

• Replace outliers with the mean of the dataset

• Ignore outliers completely and proceed with analysis

What is the role of data validation in the data cleaning process?

• Data validation is not necessary in data cleaning

• Data validation is only useful for data analysis, not cleaning

• Data validation only adds complexity to the process

• Data validation plays a crucial role in ensuring the accuracy and quality of the data being cleaned.

Discuss the significance of data normalization in data cleaning.

• Data normalization is not necessary for data cleaning

• Data normalization can introduce errors in the data

• Data normalization slows down the data cleaning process

• Data normalization is crucial in data cleaning as it reduces redundancy, improves data consistency, and facilitates data analysis.

What are some tools or software used for data cleaning in AP CSP?

• Microsoft Word

• Excel, Google Sheets, OpenRefine, Python libraries like Pandas

• Tableau

• Photoshop

Explain the difference between data cleaning and data preprocessing.

• Data cleaning involves adding noise to the dataset, while data preprocessing involves removing noise.

• Data cleaning is done before data preprocessing in the data analysis pipeline.

• Data cleaning focuses on transforming the data for analysis, while data preprocessing focuses on handling errors in the dataset.

• Data cleaning focuses on handling errors in the dataset, while data preprocessing focuses on transforming the data for analysis.

How does data cleaning contribute to the overall data analysis process in AP CSP?

• Data cleaning ensures data is encrypted before analysis.

• Data cleaning ensures accurate, complete, and formatted data for reliable analysis.

• Data cleaning involves creating duplicate data for analysis.

• Data cleaning focuses on removing outliers from the dataset.