This Exercise Involves You Working With An Already Acquired

This Exercise Involves You Working With An Already Acquired Dataset T

This exercise involves you working with an already acquired dataset to undertake the remaining three key steps of examining, transforming and exploring your data to develop a deep familiarisation with its properties and qualities. Complete the "Olympic Medalists" exercise located at the following link: Working With Data. Provide at least a 3-5 page paper for each dataset, covering examination, transformation, and exploration.

Paper For Above instruction

Examination: Articulate the meaning of the data, its representativeness, and the phenomenon it depicts. Thoroughly examine the physical properties of the dataset, including data type, size, and condition, documenting detailed descriptions of each aspect. Compare what the datasets offer and contrast their differences, highlighting unique qualities and limitations. This step aims to develop a comprehensive understanding of the dataset’s structure and scope, providing insights into its suitability for analysis.

Transformation: Identify necessary data cleaning or modification steps. Discuss potential data cleaning procedures such as handling missing values, correcting inconsistencies, normalizing data, or encoding categorical variables. Consider what additional data could enhance the dataset; for example, incorporating demographic or contextual information might deepen insights. This step involves planning practical approaches to prepare the data for meaningful analysis, ensuring its reliability and completeness.

Exploration: Use a data visualization tool of your choice (Excel, Tableau, R, etc.) to explore the datasets separately. Focus on visualizing physical properties and uncovering insights that deepen understanding of data quality and structure. Emphasize how to make data visualizations more effective: ensuring clarity, revealing structure, and emphasizing key patterns.

Discuss techniques to make data stand out, such as selecting appropriate scales, transforming data for clarity, and eliminating chart clutter. Address strategies like overplotting, jittering, and transparency to improve visual clarity. Use color, symbols, and labels strategically to convey additional information and provide context. Add reference markers and self-contained captions that describe significant features and summarize insights, thereby enhancing interpretability.

This comprehensive approach aims to foster a thorough understanding of the dataset’s properties and potential, supporting insightful data analysis and storytelling through visualization. These steps are essential for transforming raw data into meaningful information that can inform decision-making and research conclusions.

References

  • Kirk, A. (2016). Data visualization: A successful design process. Packt Publishing.
  • Wickham, H., & Grolemund, G. (2016). R for Data Science. O'Reilly Media.
  • Cleveland, W. S. (1993). Visualizing data. Summit Books.
  • Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
  • Anzai, Y. (2018). Visualizing data: Exploring and analyzing data with graphics. Springer.
  • Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.
  • Heer, J., Bostock, M., & Agrawala, M. (2010). Declarative language design for interactive visualization. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–1148.
  • Haroz, S., & Heer, J. (2018). How crowdworkers perceive the usefulness of data visualizations. IEEE Transactions on Visualization and Computer Graphics, 24(1), 326–336.
  • Rudin, C. (2013). The Statistically Informed Data Scientist. Journal of Computational and Graphical Statistics, 22(3), 512–519.
  • Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. Proceedings of the IEEE Symposium on Visual Languages, 336-343.

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