NCA-GENM Question 61
Select 3A data science team is tasked with analyzing a large dataset of customer interactions to identify trends and patterns that can improve sales. Which of the following techniques are most appropriate for extracting actionable insights from this dataset?
- A
Using data visualization tools to identify trends and correlations in customer behavior
- B
Applying data mining techniques to uncover hidden patterns and relationships
- C
Manually reviewing a subset of the dataset to find actionable insights
- D
Leveraging statistical modeling to predict future customer behavior based on historical data
- E
Ignoring outliers to focus only on the majority of the data points
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets requires a combination of automated techniques such as data visualization, data mining, and statistical modeling. These methods allow for efficient analysis of trends, patterns, and future predictions, which are critical for making data-driven decisions. Manual review and ignoring outliers are not effective or reliable approaches for handling large datasets.
- A. Correct.
Data visualization tools are essential for summarizing large datasets and revealing trends, correlations, or anomalies, making them a key technique for insight extraction.
- B. Correct.
Data mining techniques are designed to uncover hidden patterns and relationships within large datasets, making them highly appropriate for this task.
- C. Incorrect.
Manually reviewing a subset of the dataset is inefficient for large datasets and is prone to bias, making it unsuitable for extracting comprehensive insights.
- D. Correct.
Statistical modeling helps predict future behaviors and trends based on historical data, which is crucial for actionable insights.
- E. Incorrect.
Ignoring outliers can lead to missing critical insights as outliers often reveal important trends or exceptions in the dataset.