NCA-GENL Question 13
Select 3A data scientist is working with a large dataset containing customer purchase history and wants to identify purchasing patterns to improve product recommendations. Which of the following steps should they take to effectively extract insights from the dataset?
- A
Apply data visualization techniques to identify trends and anomalies in customer purchasing behavior.
- B
Use data mining algorithms such as association rule mining to uncover relationships between products.
- C
Directly deploy the dataset into a recommendation system without preprocessing or analysis.
- D
Normalize and clean the dataset to handle missing values and inconsistencies before analysis.
- E
Rely solely on domain expertise to infer patterns without computational techniques.
Show answer and explanation
Correct answers: A, B, D
Explanation
To extract meaningful insights from large datasets, a combination of preprocessing (e.g., cleaning and normalization), computational techniques (e.g., data mining and visualization), and domain expertise is necessary. This ensures the data is prepared, analyzed effectively, and interpreted in a contextually relevant manner.
- A. Correct.
Data visualization is essential for identifying patterns, trends, and outliers in the dataset, which helps in guiding further analysis.
- B. Correct.
Data mining techniques, such as association rule mining, are specifically designed to uncover hidden relationships within large datasets, making them crucial for this task.
- C. Incorrect.
Deploying the raw dataset without preprocessing or analysis is not recommended as it may contain noise, inconsistencies, or irrelevant information that can degrade the performance of the recommendation system.
- D. Correct.
Normalizing and cleaning the dataset ensures that the data is in a usable format, which is a critical step before applying data mining or visualization techniques.
- E. Incorrect.
While domain expertise is valuable, relying solely on it without leveraging computational techniques can lead to incomplete or biased insights.