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How is Big Data applied in real-world scenarios?
Analyzing customer behavior in e-commerce, tracking global shipping, predicting disease outbreaks.
How is Metadata applied in real-world scenarios?
Organizing digital photo libraries, managing music collections, improving search engine results.
How is Data Cleaning applied in real-world scenarios?
Ensuring accurate customer databases, validating survey responses, standardizing medical records.
How is Data Bias awareness applied in real-world scenarios?
Creating fair algorithms for loan applications, ensuring equitable hiring processes, developing unbiased AI systems.
What is the definition of Data?
Raw, unorganized facts that need to be processed.
What is the definition of Information?
Data that has been processed to find trends, connections, and solutions.
What is the definition of Big Data?
Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.
What is the definition of Metadata?
Data that provides information about other data.
What is the definition of Scalability?
A system's ability to adapt to increasing or decreasing data loads.
What is the definition of Server farm?
A cluster of computer servers acting as a single system to meet intense processing needs.
What is the definition of Data center?
A facility used to house computer systems and associated components, such as telecommunications and storage systems.
What is the definition of Data Cleaning?
The process of making data uniform by eliminating inconsistencies and removing invalid data.
What is the definition of Data Bias?
A systematic error that skews results in a particular direction, often due to flawed sampling or data collection methods.
What is the definition of Correlation?
A statistical measure that expresses the extent to which two variables are linearly related.
What are the general steps of data cleaning?
1. Identify inconsistencies. 2. Eliminate inconsistencies. 3. Flag or remove invalid data. 4. Flag or remove incomplete data.
What are the steps to mitigate bias in a dataset?
1. Identify potential biases. 2. Collect data from diverse sources. 3. Adjust data collection methods. 4. Re-evaluate data representation.