Creative Data Visualization: Pros & Cons - Free Paper Sample

Introduction

The data presentation depends on the information that is being presented to the audience. The visualization of data is best when it is audible enough and gives the précised content of the information being projected. There are many ways of visualizing data content in a piece of visual, graphical information (Bach et al., 2016). The best data visualization is the graphical presentation in the graphic format (Chess, 2017). Despite being the best, the are many effects of the presentation and understanding.

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Best Data Visualization

The pictorial, graphical representation in the statistics is the best visualization of the data (Bruner et al., 2020). The graphical and the pictorial representation of the statistics in the visualization of the data give précised data in the analysis (Rosling, 2018). The visual identification is clear on identification in the pictorial data in a graph that gives the best understanding (Smith et al., 2018).

Data Visualization Effects on Learning

Data visualization has excellent effects on learning. The effects of data visualization are essential in learning (Szafir et al., 2016). It gives a clear elaboration of the information being presented statistically (Chess, 2017). The decision-making after the analysis of the visualization of data is presented in graphs gives clear elaboration (Büscher, 2018). The learner can understand the pictorial representation clearly with no compromise.

Data Visualization Effects on Understanding

The visualization of data has different effects on understanding. The visual graphs give a clear understanding of statistical data in precise memorization and are helpful on the grasp scrutiny of data (Echeverria et al., 2018). Statistic presentation has an elaborative understanding of giving the picture of data (Rosling, 2018). The presentation information gives an understanding of memorizing and giving the graphs on the statistics data.

References

Bach, B., Dragicevic, P., Archambault, D., Hurter, C., & Carpendale, S. (2016). A descriptive framework for temporal data visualizations based on generalized space-time cubes. Computer Graphics Forum, 36(6), 36-61. https://doi.org/10.1111/cgf.12804

Bruner, M. W., Eys, M., Carreau, J. M., McLaren, C., & Van Woezik, R. (2020). Using the team environment assessment (TEAM) to enhance team building in sport. The Sport Psychologist, 34(1), 62-70. https://doi.org/10.1123/tsp.2018-0174

Büscher, C. (2018). Mathematical literacy in statistics. Mathematical Literacy on Statistical Measures, 7-29. https://doi.org/10.1007/978-3-658-23069-2_2Chess, S. (2017). The queer case of video games: Orgasms, Heteronormativity, and video game narrative. Queer Technologies, 84-94. https://doi.org/10.4324/9781315222738-7

Echeverria, V., Martinez-Maldonado, R., Buckingham Shum, S., Chiluiza, K., Granda, R., & Conati, C. (2018). Exploratory versus descriptive visual learning analytics: Driving teachers' attention through educational data storytelling. Journal of Learning Analytics, 5(3). https://doi.org/10.18608/jla.2018.53.6

Rosling, H. (2018). The best stats you have ever seen. TED: Ideas worth spreading. https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen

Smith, K., Piccinini, F., Balassa, T., Koos, K., Danka, T., Azizpour, H., & Horvath, P. (2018). Phenotypic image analysis software tools for exploring and understanding big image data from cell-based assays. Cell Systems, 6(6), 636-653. https://doi.org/10.1016/j.cels.2018.06.001

Szafir, D. A., Haroz, S., Gleicher, M., & Franconeri, S. (2016). Four types of ensemble coding in data visualizations. Journal of Vision, 16(5), 11. https://doi.org/10.1167/16.5.11