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Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Vitaly Friedman (2008) the "main ...
Statistics educators have cognitive and noncognitive goals for students. For example, former American Statistical Association (ASA) President Katherine Wallman defined statistical literacy as including the cognitive abilities of understanding and critically evaluating statistical results as well as appreciating the contributions statistical thinking can make.
It allows to link multiple representations dynamically. For example, changing a formula can instantly change the graph, the table of values, and the text read-out for the function represented in all these ways. Technology use can increase accuracy and speed of data collection and allow real-time visualization and experimentation. [14]
In addition, the choice of appropriate statistical graphics can provide a convincing means of communicating the underlying message that is present in the data to others. [1] Graphical statistical methods have four objectives: [2] The exploration of the content of a data set; The use to find structure in data; Checking assumptions in statistical ...
The NCBI article titled "Teaching Data Science to Medical Students: A Multi-Disciplinary Approach" (NCBI article [3]) covered how Google Charts has helped medical students learn about data science in a hands-on way. Google Charts allows for the visualization of complex data sets, which may improve some students' understanding of the information.
Students learn to use graphical and numerical techniques to analyze distributions of data (including univariate, bivariate, and categorical data), the various methods of data collection and the sorts of conclusions one can draw therefrom, probability, and statistical inference (point estimation, confidence intervals, and significance tests).
Support the selection of appropriate statistical tools and techniques; Provide a basis for further data collection through surveys or experiments [7] Many EDA techniques have been adopted into data mining. They are also being taught to young students as a way to introduce them to statistical thinking. [8]
Today, students must be able to present and decode written and visual images, presenting educators with the task of teaching visual literacy in the classroom. Students today are using PowerPoint, PhotoStory, MovieMaker and other tools to create presentations in the classroom. This presents a challenge to educators as they seek to empower their ...