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10: Reporting Results

Last updated

Apr 22, 2025
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- 9.8: Critical Thinking
- 10.0: Introduction

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10.0: Introduction
This page emphasizes the importance of data science reports for evidence-based insights and decision-making. It highlights the need for effective communication of findings to varied audiences and offers skills for creating informative reports. Using the diabetes_data.csv dataset, the chapter illustrates how Python can facilitate report creation, featuring a study on coronary heart disease risk factors among Black residents of Virginia.
10.1: Writing an Informative Report
This page discusses effective technical reporting in data science, focusing on audience adaptation, essential report components, and the use of visuals for clarity. It highlights the importance of acknowledging biases and limitations while promoting inclusivity for diverse learning styles. The analysis of health data identifies key predictors for coronary heart disease and diabetes, despite challenges like data sparsity.
10.2: Validating Your Model
This page outlines essential learning outcomes in data science modeling, highlighting the significance of assumption documentation, error measures, sensitivity analysis, and ethical considerations. It emphasizes model evaluation techniques, including regression and classification metrics, and warns against overfitting, suggesting validation methods. Furthermore, analytical methods like MPE, AIC, and BIC are discussed for decision-making and model reliability.
10.3: Effective Executive Summaries
This page explains the purpose and structure of executive summaries, highlighting their key features to engage diverse audiences. It emphasizes starting with a business problem, outlining methodologies, presenting clear findings, and concluding with actionable recommendations. The summaries should be concise and free of jargon, encouraging further exploration of reports, and supporting informed decision-making in data science analyses.
10.4: Key Terms
This page provides definitions of key terms in data science and reporting, emphasizing actionable advice and communication tailored to various audience types. It covers methodologies like bootstrap sampling and cross-validation, as well as tools such as executive dashboards and validation systems. The text highlights the importance of performance metrics, data analysis techniques, and organized reporting strategies to effectively convey information to both specialists and nonspecialists.
10.5: Group Project
This page describes a business analyst role within a grocery retailer's marketing department aimed at reversing declining sales. The project involves developing a classification model to identify potential customers for new food items, with a focus on maximizing campaign profitability. Key activities include data exploration, model validation, team collaboration for analysis and visualization, and delivering an executive summary with findings and recommendations regarding the model's efficacy.
10.6: Chapter Review
This page outlines best practices for crafting technical data science reports for varied audiences, emphasizing concise communication, avoidance of jargon, and effective use of visual aids. It highlights documenting assumptions, a layered approach for accessibility, and metrics beyond accuracy. An executive summary should clearly address the business problem, methodologies, and provide a strong conclusion.
10.7: Critical Thinking
This page outlines strategies for technical writers to enhance data science report accessibility for both experts and non-specialists, especially executives. Key points include the significance of version control, effective visual aids, clear documentation of methodologies, communication of assumptions, interpretation of statistical measures, and the importance of sensitivity analyses.
10.8: References

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