Present a great story for data science projects

This is a Slide Template

Students names (Advisor: Dr. Cohen)

2024-08-17

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 (Efromovich 2008). Cite this paper (Bro and Smilde 2014). The GEE (Wang 2014). The PCA (Daffertshofer et al. 2004)*

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

Bro, Rasmus, and Age K Smilde. 2014. “Principal Component Analysis.” Analytical Methods 6 (9): 2812–31.
Daffertshofer, Andreas, Claudine JC Lamoth, Onno G Meijer, and Peter J Beek. 2004. “PCA in Studying Coordination and Variability: A Tutorial.” Clinical Biomechanics 19 (4): 415–28.
Efromovich, S. 2008. Nonparametric Curve Estimation: Methods, Theory, and Applications. Springer Series in Statistics. Springer New York. https://books.google.com/books?id=mdoLBwAAQBAJ.
Wang, Ming. 2014. “Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments.” Advances in Statistics 2014.