NCA-GENL Question 15
Select 3A data science team is tasked with analyzing a large dataset containing millions of customer transactions to identify purchasing trends. They decide to use a combination of data mining techniques and data visualization to present their findings. Which of the following approaches are most appropriate for extracting actionable insights from this dataset?
- A
Use clustering algorithms to group customers based on their purchasing behavior.
- B
Apply dimensionality reduction techniques to simplify the dataset for visualization and analysis.
- C
Manually inspect individual transaction records to identify patterns.
- D
Create interactive visual dashboards to explore trends and anomalies in the data.
- E
Randomly sample a small portion of the dataset to perform the analysis.
Show answer and explanation
Correct answers: A, B, D
Explanation
To analyze large datasets effectively, scalable techniques such as clustering, dimensionality reduction, and interactive visualization are essential. These methods allow data scientists to uncover hidden patterns and trends efficiently. Manual inspection or random sampling is not practical for extracting insights from large, complex datasets.
- A. Correct.
Clustering algorithms, such as K-means, are effective for grouping customers with similar purchasing patterns, which can reveal trends and segmentation opportunities.
- B. Correct.
Dimensionality reduction techniques, like PCA (Principal Component Analysis), help simplify high-dimensional datasets, making it easier to visualize and analyze large amounts of data.
- C. Incorrect.
Manually inspecting individual transaction records is impractical and inefficient for large datasets, as it does not scale well and may miss broader trends.
- D. Correct.
Interactive visual dashboards, built using tools like Tableau or Power BI, allow users to dynamically explore trends and detect anomalies in the dataset, making them a powerful tool for insight extraction.
- E. Incorrect.
Random sampling may result in missing important patterns or trends in the data, especially if the sample is not representative of the entire dataset.