Data Mining Worksheet

What is Data Mining?

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Explore the risks and benefits of data mining.

• Data mining can provide valuable insights but also poses privacy risks.

• Data mining only has benefits and no risks.

• Data mining is illegal in all countries.

• Data mining is only used for marketing.

List two benefits of data mining.

• Improved decision-making and fraud detection.

• Increased manual labor and higher costs.

• Reduced data accuracy and slower processing.

• Limited access to information and decreased productivity.

How does data mining help in fraud detection?

• Data mining can identify anomalies in data, allowing enterprises to detect fraud or irregularities early on.

• Data mining increases the amount of manual work required to detect fraud.

• Data mining hides fraudulent activities from detection systems.

• Data mining only helps in organizing data, not in detecting fraud.

What is one risk of data mining related to privacy?

• Data mining can infringe on individual privacy if sensitive personal data is collected or analysed without consent.

• Data mining always guarantees the security of all personal data.

• Data mining only uses publicly available information and never private data.

• Data mining eliminates the need for any data protection measures.

What can happen if data mining is poorly executed?

• Poorly executed data mining can lead to incorrect conclusions, resulting in flawed decisions.

• It will always improve the accuracy of predictions.

• It guarantees the discovery of valuable insights.

• It ensures data privacy and security.

How can data mining perpetuate bias and discrimination?

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Analyse the impact of data scale, including volume of raw data, storage, real-time and continuous streaming, opportunities for machine learning (ML), changes in human behaviour, and ethical implications including digital footprints.

• Data scale affects storage, processing, ML opportunities, human behaviour, and ethics.

• Data scale only impacts storage requirements.

• Data scale is only relevant for machine learning.

• Data scale has no ethical implications.

Fill in the blank: The amount of data being generated, often in unstructured forms, requires significant resources and strategies for efficient analysis. Large volumes demand larger storage and processing capabilities, making traditional approaches ________.

• insufficient

• efficient

• unnecessary

• unaffected

What is required for extracting insights from continuous data streams as data is generated?

• Batch processing

• Real-time analytics

• Manual data entry

• Offline analysis