1.
In a technical data science report, what type of communication is most suitable for executives?
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Highly technical and detailed
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Focused on practical application
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Concise with a focus on summaries and outcomes
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Broad and nontechnical for general understanding
2.
What should a technical report for nonspecialists avoid?
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Complex statistical data
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Technical jargon
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Visual aids like graphs and charts
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Descriptions of practical applications
3.
Which is a key consideration when writing technical data science reports?
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Using complex language to demonstrate expertise
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Connecting with diverse audiences through tailored communication
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Focusing only on the most advanced techniques
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Avoiding visual aids to prevent confusion
4.
For technicians, a data science report should primarily focus on:
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Theoretical knowledge
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Practical application and procedures
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Advanced research and new findings
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Business implications and strategies
5.
What is the purpose of a layered approach in a data science report?
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To provide different levels of detail for various audiences
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To simplify the report writing process
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To focus solely on the technical aspects
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To avoid including any technical language
6.
What is the primary role of visual aids like graphs and tables in a data science report?
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To replace written content
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To add aesthetic appeal to the report
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To clarify and accentuate significant data insights
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To demonstrate the author's technical skills
7.
In a technical report, how should graphs and tables be presented to the reader?
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In a complex and detailed manner
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Randomly placed throughout the report
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Without any accompanying explanations
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With clear labels, titles, and straightforward explanations
8.
What is the primary purpose of documenting assumptions in a data science report?
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To prove the assumptions are correct
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To steer the decision-making process
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To increase the complexity of the report
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To show the expertise of the report writer
9.
What is a key characteristic of a well-stated assumption in a technical report?
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It is based on personal opinion.
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It is specific and verifiable.
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It is vague and general.
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It is always proven to be true.
10.
In the context of measuring house prices, what does an R-squared value of 0.75 indicate?
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The model predicts housing prices with 75% accuracy.
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Seventy-five percent of the variance in housing prices is explained by the model.
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The model is 75% reliable.
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The model will increase housing prices by 75%.
11.
Why is it important to consider additional metrics beyond accuracy in a classification model?
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To comply with industry standards
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To understand the model's performance more deeply
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To make the report longer
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To use up more computational resources
12.
What is the purpose of conducting a sensitivity analysis in a data science project?
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To test the robustness of results against input variability
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To reduce the number of input variables
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To increase the complexity of the model
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To make the report more appealing
13.
What is the primary purpose of an executive summary in a data science report?
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To present complex technical analyses in a detailed manner
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To provide a comprehensive background of the data scientists
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To summarize complex analyses for an audience of varied backgrounds
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To list all the data sources and methodologies in detail
14.
14. In a data science report, what should the executive summary ideally begin with?
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A detailed description of the data
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An introduction to the business problem or research question
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Advanced statistical formulas
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The names and credentials of the data scientists
15.
What aspect of the methodologies should be highlighted in the executive summary of a technical data science report?
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The complexity of the methodologies
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The novelty or relevance of the approach
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Every step in the methodological process
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The technical language used in the methodologies
16.
How should the executive summary in a data science report conclude?
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With a persuasive call to action
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By summarizing the data sources used
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With a detailed technical explanation
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By introducing new topics