Mastering Data Cleaning Techniques

What is the primary goal of data cleaning?

• To increase data redundancy.

• To ensure data accuracy and quality.

• To enhance data visualization.

• To simplify data storage.

What does outlier detection help to identify in a dataset?

• Trends over time in the dataset

• Common patterns in the data

• Anomalies or unusual data points in a dataset.

• The average value of the dataset

Why is it important to standardize data formats during cleaning?

• To make the data visually appealing for presentations.

• To ensure data is stored in a specific database format.

• It is important to standardize data formats to ensure consistency and accuracy in data analysis.

• To reduce the size of the data files.

What is the purpose of removing duplicates in a dataset?

• To make data more complex for analysis.

• To ensure data integrity and improve analysis accuracy.

• To increase the size of the dataset.

• To eliminate all data points from the dataset.

How can you identify inconsistencies in categorical data?

• Inconsistencies in categorical data can be identified by checking for duplicates, missing values, invalid categories, typos, and unexpected distributions.

• Checking for numerical values

• Validating data against a database

• Analyzing time series data

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

• Data validation plays a crucial role in identifying and correcting errors in the data before cleaning.

• Data validation has no impact on data accuracy.

• Data validation is used to enhance data visualization.

• Data validation is only necessary after data cleaning.

What is the difference between data cleaning and data transformation?

• Data transformation is solely about data visualization.

• Data cleaning and transformation are the same process.

• Data cleaning is only about removing duplicates.

• Data cleaning focuses on improving data quality, while data transformation focuses on changing data format or structure.

What is the impact of poor data quality on analysis outcomes?

• Poor data quality results in inaccurate analysis outcomes, leading to misleading insights and poor decision-making.

• Data quality has no effect on decision-making.

• High data quality guarantees perfect insights.

• Poor data quality enhances analysis accuracy.