A data scientist needs to analyze a company's chemical businesses and is using the master database of the conglomerate company. Nothing in the data differentiates the data observations for the different businesses. Which of the following is the most efficient way to identify the chemical businesses' observations?
Answer(s): C
Engaging the business team leverages domain expertise to pinpoint which records pertain to chemical operations, allowing you to extract and analyze just the relevant subset. This avoids the time and resource waste of ingesting and sifting through unrelated data.
Which of the following distance metrics for KNN is best described as a straight line?
Answer(s): B
Euclidean distance measures the straight-line distance between two points in space, matching the geometric "as-the-crow-flies" notion of distance.
A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?
Answer(s): A
An autoregressive model uses past values of the series itself (here, historical daily copper prices) as predictors for future values, making it the most suitable technique when only the time-series history is available.
An analyst wants to show how the component pieces of a company's business units contribute to the company's overall revenue. Which of the following should the analyst use to best demonstrate this breakdown?
A Sankey diagram visualizes flows from individual business units into the total, with the width of each flow proportional to its revenue contribution, making it ideal for showing how each component feeds the overall total.
Which of the following does k represent in the k-means model?
In k-means clustering, the parameter k directly defines how many clusters the algorithm will partition the data into.
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