NCA-GENL Question 82
Select 3A data scientist is tasked with analyzing a large dataset containing millions of customer transactions to uncover purchasing patterns. The ultimate goal is to provide actionable insights for a recommendation system. Which of the following techniques should be prioritized to extract meaningful insights during the initial exploratory phase?
- A
Data visualization to identify trends and outliers in the dataset
- B
Data mining to uncover hidden patterns and relationships between variables
- C
Deploying a pre-trained generative AI model for immediate predictions
- D
Performing dimensionality reduction to simplify data representation
- E
Randomizing the dataset to reduce bias and ensure fairness
Show answer and explanation
Correct answers: A, B, D
Explanation
The exploratory phase of data analysis involves understanding the structure and patterns within the dataset. Techniques like data visualization, data mining, and dimensionality reduction are essential for gaining insights that guide further analysis or model development. Deploying a generative AI model or randomizing the dataset is not appropriate at this stage, as they don't contribute to the initial understanding of the data.
- A. Correct.
Data visualization is critical for understanding trends, patterns, and anomalies in the dataset. It provides an intuitive overview, making it easier to identify areas for deeper analysis.
- B. Correct.
Data mining is a powerful technique for discovering hidden patterns and relationships in large datasets, which aligns with the goal of uncovering purchasing patterns.
- C. Incorrect.
Deploying a pre-trained generative AI model at this stage is premature. The initial exploratory phase focuses on understanding the data, not applying predictive models.
- D. Correct.
Dimensionality reduction can help simplify data representation by reducing the number of variables, making subsequent analysis more efficient and focused.
- E. Incorrect.
Randomizing the dataset is not relevant to the exploratory phase. While it may help during model training to reduce bias, it doesn't contribute to extracting insights from the data.