Data Transformation Challenge

What is an example of a data cleaning technique?

• Removing duplicates

• Ignoring outliers in the dataset

• Converting all text to uppercase

• Filling missing values with random data

Explain the concept of data normalization.

• Data normalization involves duplicating data to increase redundancy.

• Data normalization is only applicable to text data.

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

• Data normalization is the process of converting data into a different format.

What are some common data aggregation strategies?

• Separating

• Categorizing

• Grouping, filtering, summarizing, merging

• Combining

How does data encoding help in data transformation?

• Data encoding converts data into audio files.

• Data encoding standardizes the data format for efficient processing and communication.

• Data encoding randomizes the data for security purposes.

• Data encoding slows down data processing.

Name a popular data transformation tool used in the industry.

• Apache Spark

• Tableau

• TensorFlow

• Hadoop

Describe a data visualization technique.

• Using only text to represent data visually

• Drawing random shapes without any data correlation

• Creating 3D holograms to display data

• One data visualization technique involves creating charts, graphs, and maps to visually represent patterns, trends, and relationships within data.

Why is data cleaning important in the data transformation process?

• Data cleaning is unnecessary and adds unnecessary complexity to the process

• Data cleaning is important to ensure accuracy, consistency, and reliability of the data for analysis and decision-making.

• Data cleaning can introduce errors and inconsistencies without any benefits

• Data cleaning only focuses on aesthetics and does not impact analysis

Compare and contrast different data normalization methods.

• Mean normalization scales the data to have a mean of 1, Range normalization transforms the data to a fixed range, Decimal scaling shifts the decimal point of the data

• Min-Max Scaling scales the data to a fixed range, Z-score Standardization transforms the data to have a mean of 0 and a standard deviation of 1, and Robust Scaling is robust to outliers by using the median and interquartile range.

Discuss the role of data aggregation in big data analytics.

• Data aggregation complicates big data analytics

• Data aggregation plays a crucial role in simplifying and enhancing the efficiency of big data analytics by consolidating and organizing vast amounts of data.

• Data aggregation is irrelevant in big data analytics

• Data aggregation leads to data loss in big data analytics

How can data encoding impact machine learning models?

• Data encoding impacts machine learning models by influencing input data quality and model performance.

• Data encoding has no impact on machine learning models

• Data encoding is only relevant for traditional statistical analysis

• Data encoding only affects the visual representation of data