A data scientist is preparing to brief a non-technical audience that is focused on analysis and results. During the modeling process, the data…

When presenting to a non-technical audience focused on results, the primary goal is to convey the business value and key insights of the analysis in an understandable manner. Final charts and dashboards (A) are ideal for this, as they provide a visual, intuitive summary of the findings. Model selection, justification, and purpose (B) addresses the "why" and "so what" of the project, framing the results within a business context that is crucial for stakeholders. These two artifacts together deliver a clear message about the project's purpose and its outcomes without overwhelming the audience with technical jargon.

C. Code documentation: This is a highly technical artifact intended for developers or other data scientists, not a non-technical audience.

D. Mathematical descriptions of clustering algorithms: These are deeply technical and would be incomprehensible and irrelevant to a non-technical audience focused on results.

E. Model performance statistics: These are technical metrics (e.g., F1 score) that require a data science background to interpret correctly and are not business-focused results.

F. Data dictionary: This is a technical reference document for data structure and is too granular for a high-level results briefing.

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1. Peng

R. D.

& Matsui

E. (2016). The Art of Data Science. Leanpub. In Chapter 10

"Communication

" the authors emphasize tailoring the presentation to the audience. For non-technical stakeholders

they advise to "focus on the high-level findings and the 'story'" and to "skip the details of your methods." This supports presenting visual results (Charts/Dashboards) and the business purpose/justification over technical statistics or mathematical descriptions.

2. Saltz

J. S.

& Stanton

J. M. (2017). An Introduction to Data Science. SAGE Publications. In Chapter 11

"Communicating Data Science Results

" the text highlights the importance of data visualization for making results accessible. It states

"For a nontechnical audience

you will want to focus on telling a story with your data... Visualizations are often the best way to tell such a story" (p. 235). This directly supports the inclusion of charts and dashboards (A). The chapter also stresses explaining the project's value

which aligns with presenting the model's purpose and justification (B).

3. Shearer

C. (2000). The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing

5(4)

13-22. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a foundational methodology. In the final "Deployment" phase

the task "Produce Final Report" is described. The report for business users should summarize the process and

most importantly

present the results in a way that stakeholders can understand and use

focusing on the business objectives met. This aligns with presenting the model's purpose (B) and its results via dashboards (A)

rather than the technical implementation details.