Final Report Presentation - 225 Points Microsoft PowerPoint

A Final Reportpresentation 225 Points A Microsoft Powerpoint Docu

A final report/presentation (225 points) – a Microsoft PowerPoint document containing slides of the following types: Cover/Title Executive summary Project motivation/background Data description Data preparation activities Models used - at least three distinct techniques (with screenshots of related SAS EM output) Findings Managerial/business implications Conclusions References (if needed) There is no Word document. The report is in the form of a PowerPoint Presentation. Not just screenshots. Because the report is a PowerPoint Presentation you may be tempted to just insert a bunch of screenshots. I do want screenshots of models, results, etc. but they should be annotated so I have some sense of why they are there and what I am supposed to take from the slide. Keep it tight. Fully explaining your final project in the form of a PowerPoint (without the benefit of being able to verbally explain during a presentation) should be hard. You need to balance telling a good (and complete) story against generating excessive numbers of slides. If you were presenting I would be looking for about 15 minutes of content. Given that timeline, I would expect approximately 20 slides... this is a guideline and not an absolute, but please try and tell a complete and concise story. Tell a coherent story. The story is important... I want to know what you did and why. I want to know what models you ran and which was the best performer. I also want to know what that model says and why that is important to the organization.

Paper For Above instruction

A Final Reportpresentation 225 Points A Microsoft Powerpoint Docu

A Final Reportpresentation 225 Points A Microsoft Powerpoint Docu

This assignment requires creating a comprehensive PowerPoint presentation that effectively communicates your final project in data analysis or modeling. The presentation should encompass key components such as the cover or title slide, executive summary, project motivation and background, data description, data preparation activities, models used, findings, managerial or business implications, conclusions, and references if applicable. It is important to emphasize that only a PowerPoint presentation is required; there should be no accompanying Word document.

While incorporating screenshots of the models and analytical results from SAS Enterprise Miner (or similar tools) is necessary to substantiate your work, these screenshots must be annotated. Annotations should clarify why each screenshot is included and highlight the main takeaway for viewers, helping to tell a coherent story without the need for verbal explanation during a live presentation.

Crafting a tightly-focused and clear presentation is key. The goal is to fully explain your project within an approximately 20-slide deck, equating to about 15 minutes of presentation time. Your slides should balance completeness and conciseness; avoid excessive detail or numerous slides that dilute the main narrative. It is essential to tell a coherent story—what you did, why you did it, which models you applied, and which model performed best.

Highlight the significance of your results, especially the implications of the best-performing model for the organization, including how it could inform decision-making. Remember that your goal is to communicate your work clearly and persuasively, making it accessible to an audience that may not be familiar with technical details, but interested in actionable insights.

Paper For Above instruction

Introduction

The final project presentation is a critical component of data analytics coursework, aimed at demonstrating the ability to analyze data, develop multiple models, and communicate findings in a clear, compelling manner. The purpose of this paper is to outline the essential elements that should be included in such a presentation, emphasizing clarity, coherence, and storytelling to ensure the audience understands the research process, results, and implications.

Structure and Content of the PowerPoint Presentation

The PowerPoint slides should be organized in a logical sequence that guides viewers through the entire project journey. Starting with a cover or title slide establishes context, followed by an executive summary that succinctly highlights key insights and outcomes. The next sections should cover the motivation or background for the project, providing the rationale and problem statement.

Subsequent slides must describe the data—its source, structure, and relevant features—along with any data preparation activities taken to clean, transform, or engineer variables for analysis. The core analytical component involves presenting at least three distinct modeling techniques, such as regression, classification, clustering, or other methods pertinent to the project’s goals. For each model, include annotated screenshots of SAS EM output that illustrate model specifications, important diagnostics, and performance metrics.

Following the models, summarize the main findings derived from each, indicating which model performed best based on selected criteria (accuracy, interpretability, business relevance, etc.). The implications of these findings should then be explored from a managerial or business perspective, translating technical results into actionable insights.

The conclusion should synthesize the project’s key takeaways, reaffirm the significance of the best model, and suggest potential next steps or decisions for the organization. If any references are necessary—such as sources for data, methodologies, or supporting literature—they should be included at the end.

Key Considerations

  • Annotation: Screenshots must be annotated to clarify their purpose and relevance.
  • Storytelling: The presentation should tell a clear story, balancing completeness with conciseness.
  • Timing: Aim for about 15 minutes of presentation, approximately 20 slides.
  • Technical Content: Explain the models used, performance comparisons, and the rationale behind choosing the best model.
  • Business Focus: Emphasize how the findings impact the organization and decision-making.

Conclusion

A well-structured PowerPoint presentation that succinctly yet comprehensively covers all phases of the project, from data collection to final insights, is essential. Emphasizing clarity, storytelling, and business relevance will make the presentation impactful and valuable to organizational stakeholders.

References

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
  • Shmueli, G., Bruce, P., Gedeck, P., & Patel, N. (2019). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. Wiley.
  • Elgendy, N., & Elragal, A. (2014). Big Data Analytics: A Literature Review and Implications. Proceedings of the International Conference on Data Mining & Knowledge Management Processes.
  • Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The Elements of Statistical Learning. Springer.
  • Montgomery, D. C., & Runger, G. C. (2014). Applied Statistics and Probability for Engineers. Wiley.
  • Vapnik, V. (1998). Statistical Learning Theory. Wiley.
  • Raschka, S. (2015). Python Machine Learning. Packt Publishing.
  • Gentleman, R., & Shaffer, J. (2001). Statistical Data Analysis and Grouping. Springer.
  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.

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