Present a great story for data science projects

This is a Slide Template

Students names (Advisor: Dr. Cohen)

2025-10-23

Important

Remember: Your goal is to make your audience understand and care about your findings. By crafting a compelling story, you can effectively communicate the value of your data science project.

Carefully read this template since it has instructions and tips to writing!

More information about revealjs: https://quarto.org/docs/reference/formats/presentations/revealjs.html

Introduction

  • Develop a storyline that captures attention and maintains interest.

  • Your audience is your peers

  • Clearly state the problem or question you’re addressing.

  • Introduce why it is relevant needs.

  • Provide an overview of your approach.

In kernel estimator, weight function is known as kernel function[1]. Cite this paper[2]. The GEE[3]. The PCA[4]*

Methods

  • Detail the models or algorithms used.

  • Justify your choices based on the problem and data.

Data Exploration and Visualization

  • Describe your data sources and collection process.

  • Present initial findings and insights through visualizations.

  • Highlight unexpected patterns or anomalies.

Data Exploration and Visualization

A study was conducted to determine how…

Modeling and Results

  • Explain your data preprocessing and cleaning steps.

  • Present your key findings in a clear and concise manner.

  • Use visuals to support your claims.

  • Tell a story about what the data reveals.

Conclusion

  • Summarize your key findings.

  • Discuss the implications of your results.

References

1. Efromovich, S. (2008). Nonparametric curve estimation: Methods, theory, and applications. Springer New York. https://books.google.com/books?id=mdoLBwAAQBAJ
2. Bro, R., & Smilde, A. K. (2014). Principal component analysis. Analytical Methods, 6(9), 2812–2831.
3. Wang, M. (2014). Generalized estimating equations in longitudinal data analysis: A review and recent developments. Advances in Statistics, 2014.
4. Daffertshofer, A., Lamoth, C. J., Meijer, O. G., & Beek, P. J. (2004). PCA in studying coordination and variability: A tutorial. Clinical Biomechanics, 19(4), 415–428.