A manufacturing company's data science team needs to analyze hundreds of millions of sales records for exploratory data analysis. This involves calculating descriptive statistics (mean, median, mode), conducting complex statistical tests, and visualizing data over time. The goal is to use as much data as possible while minimizing computational resources.
The suggested answer is C, leveraging BigQuery's scalability for calculations and Vertex AI Workbench for visualization and more computationally intensive analyses.
You work on the data science team at a manufacturing company. You are reviewing the company’s historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time. You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?
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