Visualizing Proportions

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Aesthetics in Data Visualization

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Aesthetics play a crucial role in data visualization. They're not just about making charts and graphs look pretty; they're about using visual elements to effectively communicate information and insights from your data Key principles of aesthetics in data visualization: Clarity and Hierarchy: The most important principle is that your visualization should be clear and easy to understand. The hierarchy of information should be clear, with the most important elements being the most prominent. Avoid visual clutter that can distract from the data. Color Palette: Color is a powerful tool that can be used to highlight important information, differentiate between categories, and create a visually appealing chart. However, it's important to use color effectively. Choose a color palette that is easy to perceive for people with different types of color vision. Avoid using too many colors, and make sure that the colors you use are meaningful in the context of your data. Formatting: C
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From Data to Visualization

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  Ugly, Bad and Wrong Figures Data visualization is a powerful tool for communicating information, but it can also be misused or executed poorly, leading to figures that are misleading, confusing, or just plain ugly. Here are some examples of "ugly," "bad," and "wrong" figures in data visualization UGLY:                 Pie charts with too many slices: Pie charts are best for showing the composition of a whole with a few major categories. When you have too many slices (more than 6-8), it becomes difficult to compare them visually and the chart becomes visually cluttered. BAD: Line chart with 3D elements and gradient colors 3D elements and excessive visual effects: 3D charts and heavy use of gradients, textures, and other visual effects can distract from the data and make the chart difficult to read.  Missing labels or unclear legends: Charts should have clear labels for axes and data points, as well as a legend that explains what each symbol or color repr
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Time Series Visualization

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Time series data A time series is a collection of data points gathered over a period of time and ordered chronologically. The primary characteristic of a time series is that it’s indexed or listed in time order, which is a critical distinction from other types of data sets. If you were to plot the points of time series data on a graph, and one of your axes would always be time. Time series metrics refer to a piece of data that is tracked at an increment in time. For instance, a metric could refer to how much inventory was sold in a store from one day to the next. Time series data is everywhere, since time is a constituent of everything that is observable. As our world gets increasingly instrumented, sensors and systems are constantly emitting a relentless stream of time series data. Such data has numerous applications across various industries. Let’s put this in context through some examples. Examples of time series analysis: Electrical activity in the brain Rainfall measurements Stock
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